Prey capture ability of mummichogs (<i>Fundulus heteroclitus</i>) as a behavioral biomarker for contaminants in estuarine systems
Bibliographic record
Abstract
Prey capture was evaluated as a behavioral biomarker of contamination by examining feeding behavior of adult mummichogs (Fundulus heteroclitus) from 13 sites. Prey capture ability was related to sediment and tissue contaminant levels and with previous genetic analyses. The levels of contaminants at a site were highly correlated with each other, confounding the impacts of individual contaminants. The number of prey (grass shrimp) captured was highest in three of the cleanest sites. Sites with the lowest capture rates were generally more contaminated. The number of captures at all sites was highly variable, with both high and low efficiencies in highly contaminated populations. A significant relationship exists between the Mdh-A(a) allele and captures, with higher captures in the southern populations. Gut content analysis of field-collected fish had grass shrimp as the largest proportion of the diet at sites whose fish had the highest laboratory capture rates. Thus, prey capture as a behavioral biomarker is ecologically relevant and corresponds to diet in the field. However, it is not especially sensitive due to great variability at each site. Behavioral differences related to overall contaminant levels rather than to specific toxicants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".