Relative influences of the metocean forcings on the drifting ice pack and estimation of internal ice stress gradients in the <scp>L</scp>abrador <scp>S</scp>ea
Bibliographic record
Abstract
Abstract Understanding the relative influences of the metocean forcings on the drift of sea ice floes is a crucial component to the overall characterization of an ice environment and to developing an understanding of the factors controlling the ice dynamics. In addition, estimating the magnitude of the internal stress gradients on drifting sea ice floes generated by surrounding ice cover is important for modeling operations, informing the design of offshore structures and vessels in ice environments, and for the proper calibration of Discrete Element Models (DEM) of fields of drifting ice floes. In the spring of 2015 and 2016, four sea ice floes offshore Makkovik, Labrador were tagged with satellite‐linked ice tracking buoys along with one satellite‐linked weather station on each floe to transmit wind speed and direction. Twenty satellite‐linked Lagrangian surface ocean current tracking buoys were also deployed in the open water adjacent to the targeted ice floes. In this paper, the dynamics of the four ice floes are explored in terms of the relative proportions which were forced by the wind, current, sea surface topography, Coriolis, and internal stress gradients. The internal ice stress gradients are calculated as residuals between the observed accelerations of the floes as measured by the tracking buoys and the sums of the other metocean forcings. Results show that internal ice stress gradients accounted for up to 50% of the observed forcing on the floes, and may have reached up to around 0.19 kPa.
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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 | 0.000 | 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) | 0.000 | 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".