Artificial Flooding Leads to Thicker and Brighter Arctic Sea Ice
Bibliographic record
Abstract
Abstract We describe and present results from a 2024/2025 field campaign that is the first to test and observe the impact of flooding and meltwater draining on Arctic sea ice over the winter growth and spring melt seasons. The campaign was conducted in Cambridge Bay, Nunavut, Canada. A 1 by 1 km fieldwork site was used, comprising three control areas, which were never flooded, and eight test areas. In these, flooding treatments were carried out by pumping seawater onto the sea ice. Some test areas were flooded once (in December or January), while others were flooded twice (in December and February, or January and February). The total area flooded was 0.25 . Additionally, one control area was used for a melt pond drainage experiment during spring. By mid May, prior to melt, flooded test areas were up to 32 cm thicker than control areas, with snow cover that was 1–13 cm thinner. Areas flooded twice exhibited greater thickening than those flooded once. During the melt period, sea ice in the flooded areas appeared brighter and showed slower melt rates, remaining thicker than that in the control areas. The drained melt pond site also brightened markedly within 1 week of borehole drilling. Comparison with a historical sea ice thickness record from Cambridge Bay indicates that a 30 cm increase corresponds to roughly the magnitude of long‐term thinning observed over the past 50 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".