Validating the strength algorithm for sub-arctic ice with field measurements from Labrador
Bibliographic record
Abstract
This report is the second of a two-year study that examines means by which to incorporate level, landfast first-year ice in the sub-Arctic into the Ice Strength Charts that are issued by the Canadian Ice Service during the summer months. It is suggested that, if sub-Arctic ice is to be included in future Ice Strength Charts, the contour lines of equal strength in the Charts should be based upon the calculated flexural strength of the ice. The most accurate approach for that calculation requires ice property measurements, which are not usually available. Alternately, a second approach was explored: data output from the thermodynamic model used by the Canadian Ice Service was used to calculate the flexural strength of the ice. Preliminary analysis showed that the air temperatures, snow and ice thickness, and ice temperatures forecast from the model were in reasonably good agreement with measurements made on first-year ice in the high Arctic and the sub-Arctic. It was suggested that output from the thermodynamic model could be used to calculate the flexural strength of first-year ice in the sub-Arctic until about mid-May, when the ice had about 35% of its maximum mid-winter strength. In the high Arctic, where the ice decay process is less complex, the forecasted data could be used to calculate the ice strength until early July, when the ice had about 10 to 15% of its maximum mid-winter strength.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".