Bibliographic record
Abstract
Shipping has played a vital role in the globalization of trade, allowing goods to be transported between continents efficiently and cost-effectively. While safety standards have improved dramatically, the increasing scale of the industry still poses threats to the marine environment. Canada’s approach to oil spill response has relied on National planning standards across the country despite certain regions transporting a disproportionate amount of oil. Area Response Planning is a new endeavor of the federal government, and led by Transport Canada (TC) and the Canadian Coast Guard (CCG), that considers the risks and conditions specific to a geographic area. This project continues that trend and has been developed for CCG’s Environmental Response program to help organize and coordinate a response to an emergency marine oil pollution event in the Halifax Harbour. To provide tangible examples of response operations, five oil spill scenarios have been created based on vessel traffic and density, past spill events in the study area, and proximity to local sensitivities. These scenarios reveal how spills under a variety of circumstances lead to different roles and responsibilities for CCG and other agencies involved in response. They also highlight the sensitivities throughout the study area that may be affected and the needed response efforts to mitigate impacts. By focusing on the unique characteristics that define a region, this project allows response planning to be specific to its geographic region and can help inform the creation of further response plans in areas of a similar geographic scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".