Parental effects in <i>Pieris rapae</i> in response to variation in food quality: adaptive plasticity across generations?
Bibliographic record
Abstract
Abstract. 1. Herbivores using seasonal resources must cope with variation in the quality of their host plants. The effects of variation in protein concentration of artificial diet and glucosinolate concentration in canola, Brassica napus , on Pieris rapae parental and progeny growth were investigated. 2. The hypothesis that parents respond to variation in food quality by altering the phenotype of their progeny to enhance progeny fitness was tested. Consistent with previous studies, P. rapae was not affected strongly by variation in the protein concentration of artificial diet and had equal mass on completing development. 3. The mass of individual eggs of P. rapae progeny was correlated negatively with the amount of protein in the diet on which parents fed. Moreover, mothers reared in extreme conditions (high and low protein) produced progeny that grew best under those conditions. These potentially adaptive parental effects were detected early in progeny growth but not later in their development. 4. Early larval growth of P. rapae was affected negatively by increasing glucosinolates in B. napus plants, although no effects of glucosinolates were detected later in growth or on the progeny's phenotype. 5. Thus, evidence is presented that variation in food quality (protein concentration) has major consequences for the progeny of P. rapae . Given the multivoltine life history of P. rapae and the seasonal differences in food quality it encounters, such parental effects may be adaptive.
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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 | 0.000 | 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 | 0.001 |
| 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".