Airborne and in-situ measurements of wave-ice interactions in the Lower St. Lawrence Estuary
Bibliographic record
Abstract
Decreasing sea ice cover in the Arctic Ocean is leading to an increase of surface wave energy. This means that waves are becoming more important to Arctic dynamics, and so understanding their interactions with sea ice is a key question for Arctic oceanography. This work presents a unique set of simultaneous observations of wave-ice interactions during an episode of ice formation and wave generation. Airborne remote sensing observed the sea and ice surface using scanning lidar data, and infrared and hyperspectral imagery. Concurrently, an autonomous catamaran measured atmospheric fluxes, near-surface turbulence, temperature, and currents. During January 2023, this instrumentation was deployed in the fetch-limited natural laboratory of the Lower St. Lawrence Estuary in order to address the questions of how ice-forming conditions influence wave generation and how ice floes attenuate wave energy. These observations are used to develop understanding of the physics of wave-ice interactions and assess the ability of spectral wave models to reproduce them. Implications for future models and larger-scale applications will be discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".