Retrogressive thaw slump activity in the western Canadian Arctic (1984-2016)
Bibliographic record
Abstract
The spatial distribution and links to climate of retrogressive thaw slump (RTS) activity over 32 years were examined using Google Earth Engine Timelapse videos for five areas in the western Canadian Arctic totalling 150 000 km2, each previously identified as having a high spatial concentration of these thermokarst landforms. Four spatial datasets run from 1984 to 2016 (Banks Island, northwest Victoria Island, Bluenose moraine, Paulatuk region), while the fifth starts in 2001 (Richardson Mountains / Peel Plateau). The total number of RTS active in the first four areas increased more than 50-fold, from 115 in 1984 to nearly 6000 in 2013. A further 573 RTS were active in this peak year in the Richardson Mountains / Peel Plateau. RTS developed most frequently adjacent to rivers (45%), with fewer on slopes (27%) or next to lakes (23%), and the smallest group at the coast (5%). However, there was considerable variation among the areas, and more than half in the Bluenose moraine and the Paulatuk region were initiated on lakeshores. High RTS initiations were linked to particularly warm summers, but once initiated, more than half of those RTS with long records remained active for more than 25 years. The impacts of this geomorphic activity included changes of colour in more than 500 lakes due to direct or indirect sediment inputs from RTS. The results show that the non-linear orders of magnitude increase from the 1980s to the 2010s previously reported for Banks Island extended across other ice-rich parts of the western Canadian Arctic.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".