Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+tasks/typeI/geomagnetic_storm_dst__dDst_dt/data/train.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeII/open_channel_flow_manning_usgs__mean_velocity/data/train.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeII/latent_heat_flux_kgdsr__LE/data/test_test.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeII/latent_heat_flux_kgdsr__LE/data/test_fit.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeII/afm_nanoindentation_spherical_zef18__force_indentation/data/train.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeII/latent_heat_flux_kgdsr__LE/data/train.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeI/trade_gravity_cepii__log_tradeflow/data/train.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeII/latent_heat_flux_kgdsr__LE/simulator/sample.csv filter=lfs diff=lfs merge=lfs -text
+tasks/typeI/galaxy_stellar_mass_sdss__log_M_star/data/train.csv filter=lfs diff=lfs merge=lfs -text

tasks/typeI/baseball_pythagorean_winpct_lahman__win_pct/simulator/state.joblib

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tasks/typeI/binary_pulsar_post_keplerian_atnf__PBDOT/simulator/state.joblib

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tasks/typeI/bird_flight_speed_alerstam__Ue/simulator/state.joblib

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tasks/typeI/bns_merger_disk_ejecta__Mdisk/data/test.csv

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tasks/typeI/bns_merger_disk_ejecta__Mdisk/data/train.csv

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tasks/typeI/bns_merger_disk_ejecta__Mdisk/eval/reference_metrics.json

@@ -0,0 +1,119 @@
+{
+  "baselines": {
+    "darc_2025_pyoperon": {
+      "equation_loc": "Eq. 3, p. 4",
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+      "failed": false,
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+      },
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+      "metrics": {
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+        "mape": 17.592700355471333,
+        "mdae": 0.03278433489808433,
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+      },
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+      "paper_ref": "summary_formula_darc_2025.md"
+    },
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+  "task": "bns_merger_disk_ejecta__Mdisk",
+  "type": "typeI"
+}

tasks/typeI/bns_merger_disk_ejecta__Mdisk/eval/validity_rubrics.json

@@ -0,0 +1,8 @@
+{
+  "task": "bns_merger_disk_ejecta__Mdisk",
+  "validity_rubrics": [
+    "predicted disk mass is non-negative",
+    "predicted disk mass does not exceed the total pre-merger binary mass M1 + M2",
+    "predicted disk mass does not increase as the lighter star compactness C1 increases, all else held fixed"
+  ]
+}

tasks/typeI/bns_merger_disk_ejecta__Mdisk/simulator/formula.py

@@ -0,0 +1,59 @@
+"""Memorization-proof VARIANT of the BNS remnant disk-mass compactness fit.
+
+Baseline (Lund 2025, Eq. 2): a SYMMETRIC log-tanh in the lighter-NS
+compactness C_1,
+
+    log10(m_disk) = alpha * tanh(beta * C_1 + gamma) + delta,
+
+which approaches its low-C_1 and high-C_1 plateaus at the SAME rate (tanh is
+odd-symmetric about its inflection point).
+
+This variant keeps the same single predictor (C_1) and the same physical
+picture — a saturating decrease of disk mass with increasing compactness —
+but replaces the symmetric tanh transition with an ASYMMETRIC Gompertz
+(double-exponential) sigmoid:
+
+    log10(m_disk) = delta + a * exp( -exp( b * C_1 + g ) ).
+
+The Gompertz transition is steep on the low-C_1 (massive-disk) side and
+shallow on the high-C_1 (disk-stripped) side, a genuinely different
+functional SHAPE: it cannot be reduced to the textbook log-tanh form by any
+choice of coefficients (tanh has a 180-degree-rotation symmetry about its
+inflection that the Gompertz double-exponential lacks). A downstream refit of
+all coefficients therefore keeps it distinct from the memorized law while
+fitting the noisy simulator data about as well (log-space R^2 ~ 0.726 vs the
+refit log-tanh ~ 0.728), with the two curves differing by ~0.13 dex across
+the calibration range.
+
+Properties (verified on the released train+test compilation):
+  * non-negative: 10**(...) is strictly positive everywhere;
+  * far below the total binary mass M1+M2 (peak ~0.21 M_sun vs total >= 2.4);
+  * monotonically non-increasing in C_1 over [0.10, 0.25] (a>0, b>0), so more
+    compact lighter stars never yield a more massive disk.
+
+Convention: paper C_light maps to the released C1 column (M_1 <= M_2).
+Output is in solar masses (the expression gives log10(m_disk / M_sun)).
+
+Type / setting: setting1_typeI; LOCAL_FITTABLE is empty.
+"""
+
+import numpy as np
+
+USED_INPUTS = ["C1"]
+PAPER_REF = "summary_formula_lund_2025.md"
+EQUATION_LOC = "variant of Eq. 2, p. 3 (Gompertz transition)"
+# === LAW_CONSTANTS — fitted on the released data by least squares (log10 space) ===
+LAW_CONSTANTS = {"a": 2.39863, "b": 97.21078, "g": -17.14547, "delta": -3.06572}
+# === OTHER_CONSTANTS — universal physics factors ===
+OTHER_CONSTANTS = {}
+LOCAL_FITTABLE = {}
+
+
+def predict(X, a=LAW_CONSTANTS["a"], b=LAW_CONSTANTS["b"],
+            g=LAW_CONSTANTS["g"], delta=LAW_CONSTANTS["delta"]):
+    C1 = np.asarray(X[:, 0], dtype=float)
+    # Gompertz (asymmetric) saturation in compactness; clip the inner exponent
+    # to avoid overflow at extreme extrapolated C1 (exp(-exp(+inf)) -> 0).
+    inner = np.clip(b * C1 + g, -700.0, 700.0)
+    log10_m = delta + a * np.exp(-np.exp(inner))
+    return 10.0 ** log10_m

tasks/typeI/bns_merger_disk_ejecta__Mdisk/simulator/sample.csv

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tasks/typeI/bns_merger_disk_ejecta__Mdisk/simulator/state.joblib

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tasks/typeI/bns_postmerger_waveform_core__f2/eval/validity_rubrics.json

@@ -0,0 +1,8 @@
+{
+  "task": "bns_postmerger_waveform_core__f2",
+  "validity_rubrics": [
+    "includes the EOS-sensitive tidal deformability effect, with larger deformability or tidal coupling lowering f2",
+    "includes the effect of the binary mass scale on the post-merger peak frequency",
+    "predicted f2 is strictly positive"
+  ]
+}

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tasks/typeI/budyko_partitioning_mach__et_over_p/simulator/state.joblib

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tasks/typeI/catchment_mean_runoff_camels__q_mean/eval/validity_rubrics.json

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tasks/typeI/exoplanet_mass_radius__R/eval/reference_metrics.json

@@ -0,0 +1,74 @@
+{
+  "baselines": {
+    "bashi_2017": {
+      "equation_loc": "Abstract, PDF p. 1; \u00a74 Results, PDF p. 6",
+      "error": null,
+      "failed": false,
+      "kind": "reference",
+      "law_constants": {
+        "M_break": 124.0,
+        "R_break": 12.1,
+        "alpha_large": 0.01,
+        "alpha_small": 0.55
+      },
+      "local_fittable": [],
+      "metrics": {
+        "log_mae": 0.09684244224206323,
+        "mae": 1.5374998856834148,
+        "mape": 0.3107236760326059,
+        "mdae": 0.8813841527773132,
+        "mse": 6.316819143863824,
+        "n_finite": 1021,
+        "r2": 0.7947888644572237,
+        "rmse": 2.5133283000562865,
+        "smape": 0.21090985362909587
+      },
+      "other_constants": {},
+      "paper_ref": "summary_formula+dataset_bashi_2017.md"
+    },
+    "chen_2016": {
+      "equation_loc": "Eq. 1-2, PDF p. 4; Eq. 6 (continuity), PDF p. 6; Eq. 11 (piecewise), PDF p. 6; Table 1, PDF pp. 7-9",
+      "error": null,
+      "failed": false,
+      "kind": "reference",
+      "law_constants": {
+        "C1": 0.003460532109506489,
+        "S1": 0.279,
+        "S2": 0.589,
+        "S3": -0.044,
+        "S4": 0.881,
+        "T1": 0.3096301674258988,
+        "T2": 2.1191930427153887,
+        "T3": 4.425463788655218
+      },
+      "local_fittable": [],
+      "metrics": {
+        "log_mae": 0.08303315294675857,
+        "mae": 1.205949588734117,
+        "mape": 0.30251983489885675,
+        "mdae": 0.2659211980991394,
+        "mse": 5.652934956820809,
+        "n_finite": 1021,
+        "r2": 0.8163561160737799,
+        "rmse": 2.377590157453721,
+        "smape": 0.17821341359484627
+      },
+      "other_constants": {
+        "M_JUP_PER_EARTH": 317.8284,
+        "M_SUN_PER_EARTH": 332946.0
+      },
+      "paper_ref": "summary_formula+dataset_chen_2016.md"
+    }
+  },
+  "derived_caps": {
+    "fit_timeout_seconds": null,
+    "max_init_size_per_param": 1,
+    "max_law_constants": 8,
+    "max_local_params": 0
+  },
+  "metric_declared": "smape",
+  "n_test_rows": 1021,
+  "reference_baseline_id": null,
+  "task": "exoplanet_mass_radius__R",
+  "type": "typeI"
+}

tasks/typeI/exoplanet_mass_radius__R/eval/validity_rubrics.json

@@ -0,0 +1,8 @@
+{
+  "task": "exoplanet_mass_radius__R",
+  "validity_rubrics": [
+    "includes hydrogen-helium self-compression that makes Jovian planets and brown dwarfs nearly Jupiter-sized",
+    "includes low-mass rocky/icy material accumulation that makes radius grow with mass",
+    "predicted radius is strictly positive for every positive mass"
+  ]
+}

tasks/typeI/exoplanet_mass_radius__R/simulator/formula.py

@@ -0,0 +1,115 @@
+"""Smooth saturating mass-radius relation (memorization-proof variant).
+
+A physically-plausible ALTERNATIVE to the Chen & Kipping (2016) four-segment
+piecewise power law. Instead of stitching hard power-law segments at sharp
+breakpoints (min/max in log-log space), this form uses a single SMOOTH curve
+built from soft log-space transitions (softplus bends). The functional shape is
+distinct from the textbook Forecaster law and survives a coefficient refit
+(softplus bends never collapse to a hard piecewise min/max).
+
+Physics captured (in log10-log10 space, x = log10(M), y = log10(R)):
+
+    y = a + b*x - c * softplus10( k1*(x - x0) )    # rocky/icy growth that
+                                                   # smoothly SATURATES toward a
+                                                   # Jupiter-sized plateau as
+                                                   # H/He self-compression /
+                                                   # electron degeneracy halts
+                                                   # radius growth
+        + g * softplus10( k2*(x - x1) )            # gentle late upturn as the
+                                                   # stellar regime is approached
+
+where  softplus10(z) = log10(1 + 10**z)  is a smooth, monotone approximation to
+max(0, z): ~0 below the transition, ~z above it. The first term gives a power
+law R ~ M^b at low mass that smoothly bends to a near-flat plateau (the giant /
+brown-dwarf "Jupiter radius" regime); the second term lets radius creep back up
+in the stellar regime. Exponentiating, R is strictly positive for every M>0
+(10**anything > 0), satisfying the positivity rubric.
+
+Contrast with the baseline (chen_2016.py):
+    Chen: log10 R = piecewise-linear in log10 M, FOUR segments joined at hard
+          breakpoints T1,T2,T3 via nested np.where (min/max kinks).
+    This: log10 R = ONE smooth analytic curve with soft (differentiable) bends.
+A downstream refit re-optimises a,b,c,x0,g,x1 but cannot turn smooth softplus
+bends into hard piecewise kinks, so the shape stays distinct after refit.
+
+Steepness constants k1, k2 set how abrupt the two bends are; they are fixed
+structural shape constants (not separately fitted scale/offset coefficients) —
+letting them float makes the fit degenerate. They live in OTHER_CONSTANTS.
+
+LAW_CONSTANTS (fitted on the released train+test M-R pairs, log-space LS):
+    a  = -0.0218 : log10 radius zero-point (10**a R_earth at M = 1 M_earth)
+    b  =  0.4894 : low-mass (rocky/icy) power-law index, R ~ M^b
+    c  =  0.0660 : depth of the giant-plateau saturation bend
+    x0 =  2.3594 : log10 mass [M_earth] of the planetary->giant saturation
+    g  = -0.0318 : amplitude of the late stellar-regime adjustment
+    x1 =  4.0542 : log10 mass [M_earth] of the giant->stellar transition
+
+Column mapping (released CSV):
+    M (M_earth) -> column "M" (input)
+    R (R_earth) -> column "R" (target, column 0)
+"""
+
+import numpy as np
+
+USED_INPUTS = ["M"]
+PAPER_REF = "summary_formula+dataset_chen_2016.md"
+EQUATION_LOC = "memorization-proof variant: smooth saturating log-log relation"
+
+# === OTHER_CONSTANTS — fixed structural shape (bend steepness) ===
+# Not fitted scale/offset coefficients; they set the abruptness of the two
+# soft transitions. Floating them makes the fit degenerate.
+OTHER_CONSTANTS = {
+    "K1": 8.0,   # steepness of the planetary->giant saturation bend
+    "K2": 4.0,   # steepness of the giant->stellar bend
+}
+
+_K1 = OTHER_CONSTANTS["K1"]
+_K2 = OTHER_CONSTANTS["K2"]
+
+# === LAW_CONSTANTS — fitted (log10-space least squares on real M-R pairs) ===
+LAW_CONSTANTS = {
+    "a":  -0.02175,   # log10 radius zero-point
+    "b":   0.48941,   # low-mass power-law index R ~ M^b
+    "c":   0.06595,   # depth of the giant-plateau saturation bend
+    "x0":  2.35938,   # log10 mass of planetary->giant saturation
+    "g":  -0.03180,   # amplitude of late stellar adjustment
+    "x1":  4.05422,   # log10 mass of giant->stellar transition
+}
+
+LOCAL_FITTABLE = {}    # Type I — no per-cluster parameters
+
+
+def _softplus10(z: np.ndarray) -> np.ndarray:
+    """Smooth, monotone log-space softplus: log10(1 + 10**z).
+
+    ~0 for z << 0, ~z for z >> 0. Numerically stable for large z via the
+    identity log10(1 + 10**z) = z + log10(1 + 10**(-z)) when z > 0.
+    """
+    z = np.asarray(z, dtype=float)
+    pos = z > 0.0
+    out = np.empty_like(z)
+    out[~pos] = np.log10(1.0 + np.power(10.0, z[~pos]))
+    out[pos] = z[pos] + np.log10(1.0 + np.power(10.0, -z[pos]))
+    return out
+
+
+def predict(X: np.ndarray, a: float, b: float, c: float, x0: float,
+            g: float, x1: float) -> np.ndarray:
+    """Predicted radius R (R_earth) for each object.
+
+    X: (n, 1) — column M (mass in M_earth).
+    Params: LAW_CONSTANTS keys passed as keyword arguments by the harness.
+    OTHER_CONSTANTS (bend steepness K1, K2) read from module namespace.
+    """
+    M = np.asarray(X[:, 0], dtype=float)
+    # Guard against non-positive masses before log10.
+    M_safe = np.where(M > 0.0, M, np.nan)
+    x = np.log10(M_safe)
+
+    log10R = (
+        a
+        + b * x
+        - c * _softplus10(_K1 * (x - x0))   # saturate toward giant plateau
+        + g * _softplus10(_K2 * (x - x1))   # gentle stellar-regime upturn
+    )
+    return np.power(10.0, log10R)   # strictly positive for every finite log10R

tasks/typeI/exoplanet_mass_radius__R/simulator/sample.csv

@@ -0,0 +1,2001 @@
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tasks/typeI/exoplanet_mass_radius__R/simulator/state.joblib

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+size 86036

tasks/typeI/galaxy_mass_metallicity_sdss__oh/simulator/state.joblib

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tasks/typeI/galaxy_stellar_mass_sdss__log_M_star/data/train.csv

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+version https://git-lfs.github.com/spec/v1
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tasks/typeI/galaxy_stellar_mass_sdss__log_M_star/eval/reference_metrics.json

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+{
+  "baselines": {
+    "bell_2003": {
+      "equation_loc": "Bell 2003 Table A7, PDF p. 22 (g-r colour, i-band column; diet-Salpeter IMF)",
+      "error": null,
+      "failed": false,
+      "kind": "reference",
+      "law_constants": {
+        "a_i": -0.222,
+        "b_i": 0.864
+      },
+      "local_fittable": [],
+      "metrics": {
+        "log_mae": 0.021314536634175388,
+        "mae": 0.5612798601420013,
+        "mape": 0.05046218406133338,
+        "mdae": 0.5377767600000007,
+        "mse": 0.35495881707898214,
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+        "r2": -1.1574351637896854,
+        "rmse": 0.5957842034486833,
+        "smape": 0.04906310310266788
+      },
+      "other_constants": {
+        "M_sun_i": 4.58,
+        "c": -0.4
+      },
+      "paper_ref": "summary_formula+dataset_bell_2003.md"
+    },
+    "taylor_2011": {
+      "equation_loc": "Taylor 2011 Eq. 7 + Eq. 8, PDF p. 20 (M_i,sun = 4.58 from parenthetical after Eq. 7)",
+      "error": null,
+      "failed": false,
+      "kind": "reference",
+      "law_constants": {
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+        "b": 0.7
+      },
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+      "metrics": {
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+        "mae": 0.2578981111800006,
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+        "r2": 0.3968372778618503,
+        "rmse": 0.31501943917161507,
+        "smape": 0.02284469136443845
+      },
+      "other_constants": {
+        "M_sun_i": 4.58,
+        "c": -0.4
+      },
+      "paper_ref": "summary_formula+dataset_taylor_2011.md"
+    },
+    "zibetti_2009": {
+      "equation_loc": "Zibetti 2009 Eq. B1 + Table B1, PDF p. 21 (g-i colour, i-band, Chabrier IMF)",
+      "error": null,
+      "failed": false,
+      "kind": "reference",
+      "law_constants": {
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+        "b_i": 1.032
+      },
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+      "metrics": {
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+        "mae": 0.5024905944520015,
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+        "mdae": 0.4732127600000009,
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+        "c": -0.4
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+    }
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+    "max_init_size_per_param": 1,
+    "max_law_constants": 2,
+    "max_local_params": 0
+  },
+  "metric_declared": "rmse",
+  "n_test_rows": 40000,
+  "reference_baseline_id": null,
+  "task": "galaxy_stellar_mass_sdss__log_M_star",
+  "type": "typeI"
+}

tasks/typeI/galaxy_stellar_mass_sdss__log_M_star/eval/validity_rubrics.json

@@ -0,0 +1,9 @@
+{
+  "task": "galaxy_stellar_mass_sdss__log_M_star",
+  "validity_rubrics": [
+    "increases the stellar mass-to-light ratio for redder broadband optical colours",
+    "uses distance-corrected i-band luminosity to set the stellar-mass scale",
+    "predicted log stellar mass increases when an otherwise identical galaxy is made brighter in every band at fixed distance modulus",
+    "predicted log stellar mass increases with distance modulus for fixed apparent magnitudes and colours"
+  ]
+}

tasks/typeI/galaxy_stellar_mass_sdss__log_M_star/simulator/formula.py

@@ -0,0 +1,107 @@
+"""Saturating colour-(M*/L) photometric stellar-mass law (memorization-proof variant).
+
+A physically-motivated VARIANT of the SDSS i-band / g-r photometric
+stellar-mass estimator that deliberately departs from the published
+linear colour-(M*/L) calibrations (Bell 2003 Table A7; Zibetti 2009
+Table B1; Taylor 2011 Eq. 7/8) in FUNCTIONAL SHAPE.
+
+Formula
+-------
+    log10(M*/M_sun) = c*(M_i - M_sun,i) + a + k * tanh(s * (g - r))
+
+    with M_i = i - DM   (absolute i-band magnitude)
+         c   = -0.4      (structural magnitude-to-log-luminosity identity)
+         s   = 0.8       (fixed colour-saturation sharpness, per mag)
+
+The distance-corrected i-band luminosity term c*(M_i - M_sun,i) sets the
+absolute stellar-mass scale exactly as in the textbook forms.  The mass-to-
+light ratio, however, is a SATURATING function of g-r colour rather than a
+straight line: log(M*/L_i) = a + k*tanh(s*(g-r)).
+
+Why this is a distinct shape — not a re-coefficiented textbook law
+------------------------------------------------------------------
+  * The textbook calibrations all use a LINEAR colour term log(M*/L_i) =
+    a_i + b_i*(g-r).  This variant uses a bounded sigmoidal (tanh) colour
+    term.  A downstream refit of the linear template a + b*(g-r) onto this
+    variant's (clean) predictions leaves a ~0.03 dex residual and a
+    multi-dex gap at the extreme blue/red colour tails — the straight line
+    cannot reproduce the tanh curvature, so the shape stays DISTINCT after
+    any coefficient re-optimisation.
+  * Equivalently: d(log M*/L)/d(colour) is CONSTANT for the textbook law but
+    DECLINES toward red and blue colours here (the M/L–colour slope
+    flattens), a qualitatively different functional family.
+
+Physical plausibility / validity
+---------------------------------
+  * Redder colours => higher M/L: for k>0, s>0 the prediction increases
+    monotonically with (g-r) at fixed M_i, so redder galaxies get a larger
+    stellar mass-to-light ratio (validity rubric 1).  The slope merely
+    saturates at extreme colours, where stellar populations approach a
+    maximum (old, metal-rich) or minimum (young) M/L plateau — a physically
+    sensible bound that the unbounded linear law lacks.
+  * Distance-corrected i-band luminosity sets the mass scale via
+    c*(M_i - M_sun,i) (validity rubric 2).
+  * Brightening every band by the same amount at fixed DM lowers M_i,
+    leaving g-r unchanged, so c*(-dm-shift) with c<0 raises log M*
+    (validity rubric 3).
+  * At fixed apparent magnitudes and colours, increasing DM lowers M_i and
+    (c<0) raises predicted log M* (validity rubric 4).
+
+LAW_CONSTANTS — fitted here (scipy curve_fit) on the released SDSS sample
+------------------------------------------------------------------------
+    a : zero-colour log(M*/L_i) offset (dex)
+    k : saturation amplitude of the colour-(M*/L) relation (dex)
+
+OTHER_CONSTANTS — structural / fixed physics factors
+    c       = -0.4 : magnitude-to-log-luminosity conversion (structural identity)
+    M_sun_i = 4.58 : i-band AB solar absolute magnitude (Taylor 2011, PDF p. 20)
+    s       = 0.8  : fixed colour-saturation sharpness (per mag)
+
+LOCAL_FITTABLE — {} (Type I: each galaxy independent, one global law).
+
+Column mapping (released CSV -> formula symbols):
+  g  -> m_g  (SDSS g-band apparent AB magnitude, Galactic-extinction-corrected)
+  r  -> m_r  (SDSS r-band apparent AB magnitude)
+  i  -> m_i  (SDSS i-band apparent AB magnitude)
+  DM -> mu   (distance modulus; M_i = i - DM)
+"""
+
+import numpy as np
+
+USED_INPUTS = ["g", "r", "i", "DM"]
+
+# === LAW_CONSTANTS — fitted on the released SDSS sample (scipy curve_fit) ===
+LAW_CONSTANTS = {
+    "a": -0.6635,   # zero-colour log(M*/L_i) offset (dex)
+    "k":  1.4228,   # saturation amplitude of the colour-(M*/L) relation (dex)
+}
+
+# === OTHER_CONSTANTS — structural / fixed physics factors ===
+OTHER_CONSTANTS = {
+    "c":       -0.4,   # magnitude-to-log-luminosity conversion; structural identity
+    "M_sun_i":  4.58,  # i-band AB solar absolute magnitude; Taylor 2011, PDF p. 20
+    "s":        0.8,   # fixed colour-saturation sharpness (per mag)
+}
+
+LOCAL_FITTABLE = {}   # Type I — no per-galaxy free parameters
+
+
+def predict(X: np.ndarray, a: float, k: float) -> np.ndarray:
+    """Predict log10(M*/M_sun) from SDSS g, r, i apparent magnitudes and DM.
+
+    X columns: [g, r, i, DM] in USED_INPUTS order.
+    params: a, k from LAW_CONSTANTS; c, M_sun_i, s read from OTHER_CONSTANTS.
+    """
+    c       = OTHER_CONSTANTS["c"]
+    M_sun_i = OTHER_CONSTANTS["M_sun_i"]
+    s       = OTHER_CONSTANTS["s"]
+
+    g  = np.asarray(X[:, 0], dtype=float)
+    r  = np.asarray(X[:, 1], dtype=float)
+    i  = np.asarray(X[:, 2], dtype=float)
+    dm = np.asarray(X[:, 3], dtype=float)
+
+    colour = g - r          # observed-frame g-r proxy for rest-frame g-r
+    M_i    = i - dm         # absolute i-band magnitude
+    # saturating (tanh) mass-to-light ratio in colour, distinct from linear law
+    return c * (M_i - M_sun_i) + a + k * np.tanh(s * colour)

tasks/typeI/galaxy_stellar_mass_sdss__log_M_star/simulator/state.joblib

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+size 20614405

tasks/typeI/geomagnetic_storm_dst__dDst_dt/data/train.csv

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+size 19169524

tasks/typeI/geomagnetic_storm_dst__dDst_dt/simulator/state.joblib

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