Examination of Ice Ridging Methods Using Discrete Particles
Bibliographic record
Abstract
The evolution of ice thickness distribution is examined using a number of Monte Carlo simulation strategies. The present paper extends the analysis of Thorndike (2000) to consider different ridging methods. Additionally, the thickness distribution is updated at regular time intervals, and taking into account the influence of strain rates on ridging. The latter aspects are needed in order to adapt the Monte Carlo calculations for use in ice forecasting models. The ice cover is represented here by a large number of discrete particles. Starting from a given initial thickness distribution, ridging is introduced by changing the thickness and area of individual particles at regular time intervals. The results indicate that relatively small changes in ridging strategies may have significant effect on the evolution of the thickness distributions. Ridging (or increasing the thickness) of particles chosen and combined at random produces appropriate thickness distribution characteristics. Ridging the thinnest particles, on the other hand, does not produce such characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".