Relationship of fish catch and composition to water quality in a suite of agriculturally eutrophic lakes
Bibliographic record
Abstract
We examined the fish community, water quality, and morphometry of 32 agriculturally eutrophic lakes. Our purpose was to explore the relationships between eutrophication and fish catch per unit effort by weight (CPUEw) and fish community composition of the six most important species in this suite of the world's most nutrient-rich lakes. We found that the CPUEwof the sum of all species increased with lake trophic status measured as chlorophyll a (r = 0.45, p < 0.009). By dissecting total fish CPUEwinto individual species and functional groups, we found that only common carp (Cyprinus carpio) and benthivore CPUEwincreased significantly with trophic status (p < 0.03 and 0.001, respectively). Sport fish (principally piscivores) decreased as a proportion of total CPUEwby approximately 50%, while benthivores, primarily common carp, increased by approximately 80% over an increase in cholorophyll a from 10 to 100 µg·L1. Common carp CPUEwwas correlated with and may have a negative influence on CPUEwof bluegill (Lepomis macrochirus) and black crappie (Pomoxis nigromaculatus) and a positive influence on white crappie (Pomoxis annularis). Our study suggests that species other than benthivores may be unable to exploit increased energy availability in hypereutrophic systems or are competitively excluded from using this increased production.
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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 | 0.001 |
| 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".