Weather and Marine Aquaculture Workers’ Safety in Atlantic Canada
Bibliographic record
Abstract
OBJECTIVE: Marine aquaculture workers are at high risk of injury and fatalities. Understanding the role of weather in occupational safety and health (OSH) in marine aquaculture is important for work design, planning, and for safety management and hazard reduction, but there is limited research on this subject. METHODS: Using findings from a review of research and grey literature and from key informant interviews and roundtable discussions in Atlantic Canada, this paper explores the impact of weather-driven hazards on marine aquaculture in Northern and temperate regions, along with the strategies employed to mitigate these impacts. RESULTS: Findings indicate primary concerns for aquaculture OSH include sun and cold exposures; working on and under surface ice; strong winds; waves; current; reduced visibility; and ice build-up. CONCLUSIONS: Key changes that could help reduce weather-related injury risk include improved forecasting capacity; improved reporting of weather conditions at the time of an incident in administrative injury and fatality data; incorporation of weather-related OSH hazards and risks in industry risk assessments; mechanization, including increased use of remote operation technologies on farm sites; and improved infrastructure standards and design.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".