Bibliographic record
Abstract
With the increasing interest to drill in extreme cold environments and the existing drilling and production of oil in extreme cold weather locations, the extent of cold regions operations are expected to increase. OSRL tasked its Cold Weather Working Group to assess and build on its capability to maintain a suitable level of competence to deliver the response services required by its members. The subsequent action was the design and delivery of a five day cold weather Standard Course and Continuation Course to OSRL staff, Global Response Network (GRN) members and industry. The courses were designed using the cold weather knowledge and experience that OSRL staff gained through their secondments in Sakhalin, Kazakhstan and Alaska. GRN members, in particular Eastern Canada Response Centre (ECRC) and Alaska Clean Seas (ACS), were also instrumental in the design and delivery of the courses. The paper will seek to present how knowledge collaboration and impartation has been integrated throughout the full training cycle which includes the preparation phase of the courses, the delivery phase in Canada with the delegates from GRN members and industry, and the evaluation phase with a view for future development courses. Much research has been conducted on theory and methods of response to oil spills in extreme cold weather environments and the challenges that can arise such as the safety elements of working on ice and in remote locations. In conclusion, the paper will highlight the elements of the Standard and Continuation Courses that are implemented into the training cycle in order to increase, and thereafter, maintain OSRL's competence and industry's preparedness.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".