Advancing the Science and Practice of Fish Kill Investigations
Bibliographic record
Abstract
Occurrences of fish kills are increasing in aquatic ecosystems worldwide, and have been attributed to natural phenomena, as well as human modification and pollution of terrestrial and aquatic environments. Despite contemporary research activities, the science of fish kill investigations is still rudimentary and has advanced little since the 1960s. Here, we highlight the complexity of fish kills and provide a critical commentary on the key challenges that must be overcome in order to advance the science of fish kill investigation. Such challenges include recognizing the potential for carry-over effects, biotic factors, and multiple stressors when conducting fish kill investigations. We recommend an interdisciplinary approach that includes recent innovations in field physiology, functional genomics, and greater reliance on fish health professionals. We also recommend additional efforts to develop databases for tracking fish kills, as well as more attempts to publish fish kill studies in the peer-reviewed literature. The recommendations that we provide will advance our ability to identify fish kill causes, and consequently allow us to implement preventative measures to reduce the frequency and magnitude of fish kills worldwide.
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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 | 0.245 | 0.480 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".