Spatiotemporal patterns of mortality events in farmed Atlantic salmon in British Columbia, Canada, using publicly available data
Bibliographic record
Abstract
Monitoring mortality is an essential strategy for fish health management. Commercial marine finfish sites in British Columbia, Canada, are required to report mortality events (MEs) to Fisheries and Oceans Canada (DFO), which makes these data publicly available. This study aimed to analyze the spatial and temporal patterns of ME composition and total MEs. Between June 2011 and June 2022, 561 MEs were reported. The annual incidence ranged from 1.36 (95% CI: 0.55-2.81) MEs per 100 active site-months in 2013 to 17.98 (95% CI: 13.26-23.84) MEs per 100 active site-months in 2022, with a broadly increasing trend over the period under consideration. The primary causes of MEs were low levels of dissolved oxygen, fish health treatments, and harmful algal blooms (HABs). Both HABs and low dissolved oxygen followed similar patterns, increasing from 2014, peaking in 2019, and declining thereafter. Treatment-related MEs were first reported in 2017 and saw a sharp increase in subsequent years, becoming the leading cause of MEs by 2020. Nearly all treatment-related MEs were linked to sea lice treatments, highlighting the urgent need for adaptive strategies to mitigate these impacts. Sites on the west coast of Vancouver Island demonstrated a higher risk of reporting MEs compared to Mainland sites, likely due to their higher levels of exposure to fluctuating oceanographic conditions. Long-term climate change and persistent periods of warming events, such as marine heat waves, are warming the oceans, altering water parameters, and likely increasing the occurrence and severity of HABs and low dissolved oxygen-related MEs. Further studies are needed to quantify the effects of ocean warming on salmon aquaculture and the resulting increase in fish mortalities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".