ARTS: a scalable data set for Arctic Retrogressive Thaw Slumps
Bibliographic record
Abstract
Retrogressive thaw slumps (RTS) are one of the most rapid abrupt thaw events that have a positive feedback on climate warming. RTS are not yet well understood because of the lack of geospatial products describing abrupt thaw distribution and changes over time in the Arctic. Although many standalone RTS digitisation data sets have been archived, it is challenging to find, access and pool the existing data sets into a comprehensive and unified one due to the lack of common data curation standards. Therefore we collected the existing RTS digitisation data sets known to date and compiled them into a scalable and uniform data set - Arctic Retrogressive Thaw Slumps (ARTS). Besides, we developed an RTS data curation framework, which provides guidelines for RTS remote sensing data digitisation, metadata formatting, RTS indexing, storage format, contribution guidelines and more. So far the ARTS data set contains around 24,000 RTS digitisations and 3,300 non-RTS background labels. This data set will empower a wide range of Arctic studies, especially beneficial for deep learning studies that are highly data-intensive.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".