Fish as sources and sinks of nutrients in lakes
Bibliographic record
Abstract
Summary 1. The release of total phosphorus (TP) and nitrogen (N in ammonium) was measured for the five most abundant fish species (>85% of biomass) in Mouse and Ranger Lakes, two biomanipulated, oligotrophic lakes in Ontario. 2. The specific release rate of both nutrients was significantly related to fish mass; log10 TP release rate (μg h−1) = 0.793 (±0.109) [log10 wet mass (g)] + 0.7817 (±0.145), and log10 N release rate (μg h−1) = 0.6946 (±0.079) [log10wet mass (g)] + 1.7481 (±0.108). 3. When fish nutrient release was standardized for abundance (all populations, 1993–95) and epilimnetic volume, fish were estimated to contribute 0.083 (±0.061) μg TP L−1 day−1, and 0.41 (±0.17) μg N L−1 day−1 in Mouse L., and 0.062 (±0.020) μg TP L−1 day−1 and 0.31 (±0.08) μg N L−1 day−1 in Ranger L. 4. In comparison, concurrent rates of total planktonic P regeneration were 1.02 (±0.45) μg L−1 day−1 (Mouse L.) and 0.85 (±0.19) μg L−1 day−1 (Ranger L.). Fish represented 8% of planktonic P release in Mouse L. and 7% in Ranger L. 5. Fish dry mass had mean elemental body compositions of 39.3% carbon, 10.9% nitrogen, and 4.0% phosphorus (all fish combined), with a mean molar C : N : P ratio of 27 : 6 : 1. This comprised about 55% and 23% of the total epilimnetic particulate P and N respectively. 6. Turnover times of P and N in fish were approximately 103 and 48 days respectively. In comparison, planktonic turnover times of particulate P in Mouse and Ranger Lakes were 4.3 and 4.4 days respectively. Given their high P content and low turnover rates, fish appear to be important P sinks in lakes.
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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 | 0.001 | 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".