General empirical models for predicting the release of nutrients by fish, with a comparison between detritivores and non‐detritivores
Bibliographic record
Abstract
Summary 1. We derived models of nutrient release [nitrogen (N) and phosphorus (P)] by fish based on studies that directly measured the release rates from 56 species across a broad range of fish mass, feeding histories and temperature. 2. We developed four separate models of nutrient release from multiple regression analysis: detritivore release rates of N and P, and non‐detritivore release rates of N and P. 3. Fish mass explained most of the variance (78–92%) in release rates. 4. Our predicted rates of release of P by fish (g ha−1 day−1) were similar to observed rates in the literature from other lakes. 5. The influence of a shift in diet (planktivory to detritivory) by a single species (gizzard shad, Dorosoma cepedianum, a facultative detritivore) on nutrient release rates was estimated. During periods of detritivory, gizzard shad accounted for on average 39% (<1–96%) of all nutrients released by the fish assemblage, and increased total fish assemblage release rates on average by 59% (<1–331%) compared to when gizzard shad were modelled as planktivores. 6. These models provide a rapid means for predicting the release of nutrients by fish assemblages and may facilitate more comprehensive comparisons of nutrient cycling by fish with other internal pathways.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".