Mortality events at British Columbia finfish aquaculture sites
Bibliographic record
Abstract
Mortality at salmon aquaculture facilities is closely monitored. If the amount of dead fish at a farm exceeds thresholds outlined in conditions of licence, a mortality event is said to have occurred and must be reported to DFO within 24 hours of discovery. Facility managers and veterinarian(s) must determine the probable cause of the event and develop a plan to mitigate ongoing harm to the farmed fish and reduce any risk to wild fish. Companies must continue to update Fisheries and Oceans Canada (DFO) every 10 days for the duration of the mortality event, including daily mortality counts, mitigation applied, determination of the cause(s) of the event and any updated plan.This report provides a summary of mortality events reported by aquaculture companies to Fisheries and Oceans Canada.Historical data are available from 2011 to the present. Explanation of the terms used in the report’s column headings can be found in the terminology file https://open.canada.ca/data/en/dataset/7fbb2662-391a-4df7-99b4-3343fa68fc93
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.029 |
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".