Temperature and the effects of elemental food quality on Daphnia
Bibliographic record
Abstract
1. We examined the responses of two species of Daphnia to changes in food phosphorus (P) content, with animals reared at three different water temperatures. Specifically, we measured mass-specific growth rate (MSGR), body P content and respiration rate of Daphnia magna and Daphnia pulex acclimatised to 10, 17.5 and 25 °C and fed food carbon : phosphorus (C : P) ratios of either 150 or 500. 2. The responses of these three physiological variables to temperature–food quality interactions were species-specific. There was a significant interactive effect of temperature and food quality on D. magna, as the greatest proportional effect of food quality on growth was observed at 10 °C and reductions in body P because of low food P content were relatively greater at 25 °C. These effects may reflect the temperature dependence of mechanisms that reduce elemental constraints associated with food quality in D. magna. By contrast, there were no interactive effects between food quality and temperature on MSGR, body P or mass-specific respiration of D. pulex. 3. It thus appears that temperature can alter food quality effects on Daphnia but the nature of these alterations depends upon the daphniid species and its thermal adaptability. Significant temperature–food quality interactions will complicate efforts to understand zooplankton nutrition in nature and warrant future consideration.
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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".