Ice ridging and ice drift in Southern Gulf of St Lawrence, Canada, during winter Storms
Bibliographic record
Abstract
Abstract During February and March 2004, Satellite-tracked ice beacons and helicopter-borne Sensors collected ice-drift and ice-thickness data from the Southern Gulf of St Lawrence, Canada, to Study the region’s ice-thickness evolution and ice-drift behavior in response to winter Storms. Three northeasterly Storms passed through the area during the observation period, pushing the pack ice against the north Shore of Prince Edward Island. The resulting Severe ice deformation caused major changes in the ice-thickness distribution of two pack-ice areas tracked by ice beacons that Survived the Storms. The ice drift ranged from 1.4% to 2.9% of the wind Speed during free ice-drift conditions, decreasing to 0% when the pack ice compacted against the Shoreline. Most of the thinner ice deformed first, increasing the mean ice thickness over 6–8 km line Sections around the beacons from 0.6 and 0.3 m before the Storms to 1.9 and 2.0 m after the Storms. The ice-thickness increases can be accounted for by the reduced pack-ice area due to ice ridging. Over the next 4weeks, deformation continued and the mean ice thickness around the beacons increased to 2.8 m, well in excess of the maximum undeformed possible ice growth of 65 cm. Ice charts captured the ice thickness of undeformed and composite ice floes but did not capture the ice volume in ice-rubble fields.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".