Pack ice stress and convergence measurements by satellite-tracked ice beacons
Bibliographic record
Abstract
Relationships between pack ice stress, ice convergence and atmospheric conditions are investigated with data collected by satellite-tracked ice beacons deployed on the pack ice off the Canadian east coast in March 1996. Ice beacons containing Global Positioning System (GPS) sensors monitored pack ice convergence and divergence. Stationary tests showed that on average the beacons provided hourly data 93% of the time with an absolute positional accuracy of 37m. The mean relative distance accuracy was 17m with a 87% data return when positions from all satellite constellations were used. When only positions from similar satellite constellation were used the relative distance accuracy was 1.7m with a 55% data return. Ice stress data were collected by a 3-directional ice stress sensor deployed on a floe that was the centre of a pack ice triangle monitoring ice convergence/divergence with GPS ice beacons. The pressure sensor facing the thick offshore pack ice recorded pressures between 20 and 40 kPa when ice temperatures at the sensor were between -3.5/spl deg/C and -2.5/spl deg/C. Multi-variate regression analysis showed that the response of the major principal stress was 0.36 kPa for each 1 m/s of wind forcing and that the wind and the ice temperature effects explained 85% of the variance in the major principal ice stress. Since at these warm ice temperatures both the ice volume and ice strength decrease when ice warms, the thermal response of the major principal stress was a decrease of 24.9 kPa for each 1/spl deg/C of ice warming.
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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 | 0.001 |
| 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) | 0.000 | 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".