Bias in self‐reported parasite data from the salmon farming industry
Bibliographic record
Abstract
Many industries are required to monitor themselves in meeting regulatory policies intended to protect the environment. Self-reporting of environmental performance can place the cost of monitoring on companies rather than taxpayers, but there are obvious risks of bias, often addressed through external audits or inspections. Surprisingly, there have been relatively few empirical analyses of bias in industry self-reported data. Here, we test for bias in reporting of environmental compliance data using a unique data set from Canadian salmon farms, where companies monitor the number of parasitic sea lice on fish in open sea pens, in order to minimize impacts on wild fish in surrounding waters. We fit a hierarchical population-dynamics model to these sea-louse count data using a Bayesian approach. We found that the industry's monthly counts of two sea-louse species, Caligus clemensi and Lepeophtheirus salmonis, increased by a factor of 1.95 (95% credible interval: 1.57, 2.42) and 1.18 (1.06, 1.31), respectively, in months when counts were audited by the federal fisheries department. Consequently, industry sea-louse counts are less likely to trigger costly but mandated delousing treatments intended to avoid sea-louse epidemics in wild juvenile salmon. These results highlight the potential for combining external audits of industry self-reported data with analyses of their reporting to maintain compliance with regulations, achieve intended conservation goals, and build public confidence in the process.
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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 | 0.058 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".