Replication Data for: Thermophysical diversity of young lunar crater ejecta revealed with LRO Diviner observations
Bibliographic record
Abstract
Contents For each crater region of interest (ROI), this dataset contains: Raw data: Bolometric temperatures and propagated uncertainties binned at 128 pixels per degree (ppd). For each original point-like observation in each detector, a Monte Carlo sampling of 100 points is projected through the calculated Diviner effective field-of-view (EFOV) onto an icosahedral mesh interpolated to the 256ppd LOLA/Kaguya TC merged DEM (SLDEM2015); see description in Williams et al. (2016) & Sefton-Nash et al. (2017) Bolometric temperatures are calculated from the binned radiances in each of the seven Diviner thermal channels (ch. 3-9) using the algorithm of Paige et al. (2010) Data are in tabular text format separated by spaces and ordered by longitude, latitude, and (average) local time of each binned observation (0-24 hr, 0 = local midnight) Bolometric temperatures and uncertainties are in units of kelvin Additional data for each 128ppd bin include: solar distance at time of measurement (AU), average emission angle of the binned observation (degrees), and number of EFOV-projected data points within the bin Bolometric temperatures are corrected for solar distance (normalized to 1 AU) GeoTIFF files: Multi-band files: RGB composite of model output in byte (0-255 normalized) and float (original model values) format, with separate folders for masked data (H1param given in file name) and original two-parameter model output. Byte format files have a 2-98% stretch applied to each channel before mapping to final values. Single-band files: Individual files for ΔTBOL, Id,273K, and H for both masked and unmasked two-parameter model output (R, G, and B channels respectively in corresponding RGB composite file) Best fit H-parameter values for the one-parameter model Root-mean-square error (RMSE) between model and data (normalized by degrees of freedom) for both the one-parameter and two-parameter models Reduced chi-squared values between model and data (weighted by bolometric temperature uncertainties) for both the one-parameter and two-parameter models Shapefiles: Shapefiles of the mask boundary between the one-parameter and two-parameter model results (H1param values given in file name)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.061 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".