Challenges in current adult fish laboratory reproductive tests: Suggestions for refinement using a mummichog (<i>Fundulus heteroclitus</i>) case study
Bibliographic record
Abstract
Concerns about screening endocrine-active contaminants have led to the development of a number of short-term fish reproductive tests. A review conducted of 62 published adult fish reproductive papers using various fish species found low samples sizes (mean of 5.7 replicates with a median of 5 replicates) and high variance (an average coefficient of variance of 43.8%). The high variances and low sample sizes allow only relatively large differences to be detected with the current protocols; the average significant difference detected was a 68.7% reduction in egg production, while only differences above 50% were detected with confidence. This result indicates low power to detect more subtle differences and a high probability of type II errors in interpretation. The present study identifies several ways to increase the power of the adult fish reproductive test in the mummichog (Fundulus heteroclitus). By identifying the peak timing of egg production (before and after the new moon), extending the duration of the experiment (increased from 7 to 14 d), and determining that a sample size of eight replicate tanks per treatment accurately predicts variance in the sample population (based on pre-exposure variation calculations of replicate tanks), the power of the test has been significantly increased. The present study demonstrates that weaknesses in the current adult fish reproductive tests can easily be addressed by focusing on improved understanding of the reproductive behavior of the test species and developing study designs that include calculating desired variability levels and increasing replicates.
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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 | 0.121 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".