Field studies using fish biomarkers – How many fish are enough?
Bibliographic record
Abstract
Significant efforts are invested in field studies using fish, and it is important to optimize the number of organisms collected to evaluate the possible impacts of contamination. This paper provides ecotoxicologists with the approximate numbers of fish needed to identify statistically significant differences among samples using physiological indices and biochemical markers of fish health. The numbers of fish to collect are reported for ethoxyresorufin-o-deethylase (EROD); ethoxycoumarin-o-deethylase (ECOD), serum sorbitol dehydrogenase (sSDH), stress proteins, gonadosomatic index, liver somatic index, condition factor, and biliary metabolites of polycyclic aromatic hydrocarbons. The number of fish to collect was as few as four for ECOD activity (with a power of 80%), but as high as 106 for CF (with a power of 95%). Achieving statistical significance between sites does not help in the interpretation of the biological significance of a parameter, but well-planned field samplings will maximize the chances of correctly identifying areas of concern.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".