Using Nearshore Fish Communities to Evaluate Eutrophication Impact in Temperate Estuaries
Bibliographic record
Abstract
Using fishes as indicators of estuarine degradation is informative in long-term monitoring programs. Beach seine hauls were used in four estuaries that differed in their trophic and tidal status. The study found that inner, middle, and outer estuarine nearshore fish communities differed significantly at from all estuaries, for all sampling times. To reduce the effects of within-estuary variability, between-estuary differences were examined separately for inner, middle, and outer estuarine areas. These analyses revealed differences in north-south and trophic status in communities between estuaries. The north-south differences were characterized by more benthic fishes, such as mummichogs and fourspine stickleback in the microtidal northern estuaries and proportionally more pelagic fishes, such as Atlantic silversides and river herring in the mesotidal estuaries. In both multivariate and univariate analyses, mummichogs were also featured as being most abundant in the more eutrophic estuaries. The distance-based redundancy analysis showed that bare sediment coverage was the strongest correlate of the north-south differences, while the Ulva to Zostera plant gradient was more influential in predicting eutrophication impacts on communities in the inner and middle estuary.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".