Neutron and LIBS data behind figures in Gabriel et al. (2022). On an extensive late hydrologic event in Gale crater as indicated by water-rich fracture halos. JGR-Planets.
Bibliographic record
Abstract
This repository contains datasets that allow for the reproduction of certain figures and analysis by Gabriel et al. (2022), a peer-reviewed journal article accepted in the Journal of Geophysical Research: Planets. Below are brief descriptions of the datasets. File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol350-400_FigureS10.txt Description: These are raw neutron counts from the thermal and epithermal neutron detectors as part of the Dynamic Albedo of Neutrons instrument. Only data from rover stops for sols 350 to 400 are included. Data from rover stops allows them to be readily colocated rover localization data, which includes 'site' and 'drive' numbers that are specific to each stop. File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol900-1500_Figure5.txt Description: This is similar data to the product above, however for the sol range 900 to 1500. File: TGabriel_JGR-P_DAN_Passive_withMobility_Raw_Data_sol350-420_FigureS13.txt Description: This is similar data to the products above, however the dataset includes passive neutron count rates acquired while the rover was traversing, smoothed over 3 meters of lateral distance traveled. This dataset allows for the analysis of environments that may be present between rover stops, and thus not detected in 'no mobility' datasets. TGabriel_JGR-P_Kukri_CCAM_MajorOxideComposition_FigureS19TableS1.xlsx Description: This is the result of the Major Oxide Quantification pipeline developed by the ChemCam instrument team (sPDL Tool v2.0, 25 July 2015) as run by William Rapin. Additional H quantification in Figure S19 of Gabriel et al. (2022) is not included in this dataset, but is provided in the manuscript.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.314 |
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".