HOW WELL CAN WE PREDICT ICE LOADS?
Bibliographic record
Abstract
A study has been performed to investigate the general level of agreement of predicting ice loads from various international experts. The format followed that of three previous studies of this type carried out in 1988, 1994 and 1996. Four simple ice loading scenarios were developed and experts in ice mechanics were invited to predict the loads. Twenty-one Predictors took part in this study. The results show that there is a considerable improvement in agreement for loads generated by a level, first-year ice sheet on a vertical-sided structure. This improvement in agreement is attributed to new full-scale data which is publicly available. There is still a large range of predicted loads from first-year ridges (factor of five) and multi-year floes (factor of seven) interacting with a vertical-sided structure. There is a large range of disagreement (factor of over eleven) on predictions of level ice on a conical-shaped structure.
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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) | 0.253 | 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".