Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic, Datasets
Bibliographic record
Abstract
Here we quantify the abundance and distribution of three primary permafrost region disturbances (PRD; lakes and their dynamics, wildfires, retrogressive thaw slumps) using trend analysis of 30-m resolution Landsat imagery from 1999-2014 and auxiliary datasets. The dataset spans four continental-scale transects in North America (Alaska, Eastern Canada) and Eurasia (Western Siberia, Eastern Siberia), covering 2.3M km² or ~10% of the permafrost region. This data publication contains geospatial vector files (polygons) of the perimeters of PRD.The data are subdivided by PRD type (lakes, wildfire, retrogressive thaw slumps) and further subdivided by study region (T1_WS, T2_ES, T3_AK, T4_EC).T1_WS: Western SIberiaT2_ES: Eastern SiberiaT3_AK: AlaskaT4_EC: Eastern CanadaThe datasets are documented in detail in the linked document (Nitze_etal_2018: Data Documentation v1.0).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".