Formation of a 1.5km Wide Ice Rubble Field from a 60cm Thick Flaw Lead in Eastern Canadian Beaufort Sea
Bibliographic record
Abstract
During April 2008, ice property data were collected with helicopter-borne sensors along flight paths over the pack ice in the eastern Canadian Beaufort Sea using a Canadian Ice breaker, CCGS Amunsden, as a logistic base. Ice thickness, surface roughness data were collected with an Electromagnetic-Laser system and lead/floe distributions with a Video-Laser system. One strong wind event generated a large linear ice rubble field when the 60cm thick flaw lead, 18km wide, was crunched into land-fast by the 1.5m thick offshore pack ice. From imagery before and after the event and from data collected by the helicopter-borne sensors it was found that the original 18km wide flaw lead became a 1.3-1.4km wide rubble field with an average thickness of 8m. The change does account for the ice volume of the original flaw lead. When the wind reversed a new flaw lead opened up leaving the newly formed rubble field attached to and become part of the original land-fast ice. The observations are an excellent validation data set for ice-ocean forecast models trying to forecast ice features such as rubble field formation that during the pack ice evolution would represent an ice hazards to navigation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".