Bibliographic record
Abstract
An iceberg drift model for the Barents Sea has been developed and the iceberg deterioration model developed by the Canadian Hydraulics Centre (CHC) has been implemented within the iceberg drift model. The deterioration model includes contributions from wave erosion, calving, solar radiation, buoyant convection and forced convection. The model relies on wave and wind data from the Norwegian hindcast archive and regional temperature and salinity recordings. Iceberg drift simulations in the Shtokman region show that wave erosion process is the main contribution to iceberg deterioration causing almost 71% of the mass reduction. Forced convection on the submerged part of the iceberg is the second most important contribution to the deterioration causing 18% of the reduction. Further, calving, which is a consequence of the wave erosion, explains about 8% of the reduction. While reduction in length of icebergs drifting in open waters may be several meters per day, the deterioration of icebergs embedded in sea ice is limited and generally less than 25 cm/day. A sensitivity study reveals that the sea surface temperature that affects both wave erosion and forced convection, is the most important parameter with respect to iceberg deterioration. Also significant wave height with associated wave period and iceberg lengths are important for the deterioration. As a part of an ice management system, logging of these parameters will be important in order to estimate the size of the iceberg when it later approaches the iceberg prevention zone. Calculations of mass loss from the wave erosion model has been compared with physical measured mass loss in a model test. Results indicate that the physical mass loss may be even more severe than estimated from the model. Further work should aim to verify the deterioration model skills in the Barents Sea. For instance, in situ observations and monitoring of real icebergs over one or two weeks would provide important information.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".