Comparative statistical analysis of simulated ice management effectiveness
Bibliographic record
Abstract
Abstract Ice management is critical for safe and efficient operations in ice-covered waters; thus, it is important to understand the impact of the operator’s experience in effective ice management performance. This study evaluated the confidence intervals of the mean and probability distributions of two different sample groups, novice cadets and experienced seafarers, to evaluate if there was a difference in effective ice management depending on the operator’s level of experience. The ice management effectiveness, in this study, is represented by the “clearing-to-distance ratio” that is the ratio between the area of cleared ice (km 2 ) and the distance travelled by an ice management vessel (km) to maintain that cleared area. The data analysed in this study was obtained from a recent study conducted by Memorial University’s “Safety at Sea” research group. With the distribution fitting analysis providing inconclusive results regarding the normality of the data, the confidence intervals of the dataset means were obtained using both parametric approaches, such as t -test, Cox’s method, and Johnson t -approach, and non-parametric methods, namely Jackknife and Bootstrap methods, to examine if the assumption of normality was valid. The comparison of the obtained confidence interval results demonstrates that the mean efficiency of the cadets is more consistent, while it is more varied among seafarers. The noticeable difference in ice management performance between the cadet and seafarer sample groups is revealed, thus, proving that crew experience positively influences ice management effectiveness.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".