Assessment of Northern Mummichog (Fundulus heteroclitus macrolepidotus) as an Estuarine Pollution Monitoring Species
Bibliographic record
Abstract
Abstract The use of multiple spawning fishes in environmental effects monitoring programs has proven difficult for a number of reasons including the inability to predict reproductive investment and ensure synchronous sampling of reference and impacted populations. The estuarine resident northern mummichog (Fundulus heteroclitus macrolepidotus) has been successfully used as a sentinel for effects of pulp and paper mill effluents in Atlantic Canada and has been proposed for monitoring other anthropogenic impacts. This study investigated the spatial and temporal variability of the somatic parameters used to describe fish performance, specifically measures of energy use and storage, in estuaries located in Prince Edward Island, Canada. Three sites with varying levels of agricultural input were studied. Fish at all sites depleted their energy reserves over winter, as reflected in depressed condition, liver size, and gonad size, but then quickly replenished them in May. These population parameters were highly variable throughout the reproductive season and within an estuary. Spawning was continuous at all sites without indication of lunar or other periodicity. We conclude that repeated sampling is required to assess reproductive output in the northern mummichog and densities of adults and young-of-the-year deserve further investigation as a potentially less logistically demanding indicator of eutrophication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".