Measuring impact crater depth throughout the solar system
Bibliographic record
Abstract
Abstract One important, almost ubiquitous, tool for understanding the surfaces of solid bodies throughout the solar system is the study of impact craters. While measuring a distribution of crater diameters and locations is an important tool for a wide variety of studies, so too is measuring a crater's “depth.” Depth can inform numerous studies including the strength of a surface and modification rates in the local environment. There is, however, no standard data set, definition, or technique to perform this data‐gathering task, and the abundance of different definitions of “depth” and methods for estimating that quantity can lead to misunderstandings in and of the literature. In this review, we describe a wide variety of data sets and methods to analyze those data sets that have been, are currently, or could be used to derive different types of crater depth measurements. We also recommend certain nomenclature in doing so to help standardize practice in the field. We present a review section of all crater depths that have been published on different solar system bodies which shows how the field has evolved through time and how some common assumptions might not be wholly accurate. We conclude with several recommendations for researchers which could help different data sets to be more easily understood and compared.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".