Statistical Analysis of the Optimum Amount of Bollard Pull Required for Towing an Iceberg
Bibliographic record
Abstract
Towing icebergs in support of exploration drilling and production facilities has minimized ice related downtime and contributed to safety when operating in harsh environments. Iceberg management operations have been conducted off Newfoundland and Labrador for over 30 years. Results of these towing operations are contained in The Program of Energy Research and Development (PERD) Comprehensive Iceberg Management Database, which holds detailed information on over 1500 iceberg management operations. Among the data contained in this publicly available database, are the tow forces (bollard pull) used on past operations dating as far back as 1973. Using the PERD database, this paper will investigate whether the there is an optimum amount of bollard pull for towing an iceberg based on past experiences. The paper concludes with the ideal bollard pull criterion to be considered by offshore oil and gas operations when determining the primary towing vessel for iceberg management operations.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".