Growth responses of littoral mayflies to the phosphorus content of their food
Bibliographic record
Abstract
We examined how mayfly growth rates and body stoichiometry respond to changing phosphorus (P) content in food. In two experiments, mayfly nymphs were given high or low quantities of food at different carbon:phosphorus (C:P) ratios and their growth was measured. Low food quantity resulted in negative growth rates in both experiments, regardless of food P content. However, under high food availability, mayfly growth was affected by the type of food eaten, with low C:P ratio food producing more rapid growth. In addition, mayfly growth increased somewhat when P‐poor food was artificially enriched with inorganic P although this effect was not statistically significant. Mayfly body P content was inversely related to body size but increased in animals fed artificially P‐enriched food. A model was constructed to simulate mass balance constraints on mayfly growth imposed by the relative supply of two elements (C and P) in food. The model shows that mayfly growth should be limited by food P content at moderately low C:P ratios (c. 120, by mass). Given high C:P ratios (mean c. 270, by mass) in periphyton from oligotrophic boreal lakes, our experimental and theoretical results indicate that stoichiometric constraints are important factors affecting benthic food webs in lakes from the Canadian Shield and perhaps in other systems with similarly high C:P ratios in periphyton.
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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) | 0.001 | 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".