Preliminary study on the applicability of the POLARIS methodology for ships operating in lake ice
Bibliographic record
Abstract
A preliminary study was performed to assess the applicability of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) for ships operating in lake ice. POLARIS provides guidance regarding operational limits for ships operating in polar waters, which was developed in association with the IMO Polar Code in 2014. POLARIS uses a Risk Index Outcome (RIO) value based on a ship's ice class and specific ice conditions, which is calculated using a Risk Index that reflects the ship's operational capability in ice. The Risk Index contains a set of Risk Index Values (RIVs) corresponding to different stages of sea ice development for each ice class. AIS records for ships operating in the North American Great Lakes region for ice seasons between 2010 and 2019 were analysed to compare historical vessel operations to the recommendations prescribed by POLARIS, by applying the POLARIS methodology to operations in lake ice instead of its normal application for polar operations. Ice condition information was extracted from the Canadian Ice Service digital charts. RIVs for lake ice types were assigned based on the closest equivalent sea ice types within POLARIS by thickness. Results from this study suggest that Great Lakes shipping operations are generally consistent with POLARIS guidelines and icebreaker support is observed to correspond well with regions where vessels most frequently encounter negative RIO values. While these results indicate that the development of modified POLARIS guidelines for the Great Lakes seem feasible, this work also highlights the need for additional information and further analysis. Areas requiring further investigation include assessing potential for ship damage from lake ice versus sea ice of equivalent thickness, establishing POLARIS Risk Index Values specific to lake ice stages-of-development, and assessing suitable RIVs that reflect operations in freshwater ice. Additional information needed to support this work includes documentation of the ice class of all vessels operating in this region, details of local operational ice conditions (rather than general ice conditions obtained from ice charts), and information about the extent and frequency of traffic that uses shipping lanes in broken ice channels. Additional areas to be explored include evaluation of optimum speed limits for different operational scenarios and the degree to which icebreaker support is necessary to operate a given class of vessel in particular ice conditions.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".