Bibliographic record
Abstract
Abstract. We provide high-resolution in situ observations of wave-induced sea ice breakup in the natural environment. In order to obtain such data, a drone was deployed from the Canadian Coast Guard ship Amundsen as it sailed in the vicinity of large ice floes in Baffin Bay and in the St. Lawrence Estuary, Canada. The footage recorded during these experiments was used to obtain the floe size distribution (FSD) and the temporal evolution of the breakup. Floe-area-weighted FSDs exhibit a modal shape, indicating that a preferential size is generated by wave-induced breakup. Furthermore, the increase of the mode of the distribution with greater thickness indicates that ice thickness plays a defined role in determining the preferential size. Comparison with relevant theory suggests that the maximum floe size is dictated not only by the ice rigidity but also by the incident wavelength. It was also observed that the in-ice wavelength is smaller than the estimated incident wavelength, suggesting that waves responsible for the breakup obey mass loading dispersion. The fact that the breakup advances almost as fast as the wave energy suggests that fatigue might not have been an important physical component during the experiments. Moreover, the observed breakup extents show that thicker ice can attenuate waves less than thinner ice. Overall, this dataset provides key information on wave-induced sea ice breakup and highlights the potential for better understanding the physics of natural sea ice in response to waves.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.283 | 0.167 |
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".