Data from: The influence of environmental variance on the evolution of signalling behavior
Bibliographic record
Abstract
A recent meta-analysis has indicated that environmental quality and variability can influence whether offspring begging and parental responses to these signals are motivated by offspring need or offspring quality. We create a model to verify and apply evolutionary logic to this hypothesis. We determine the ecological and social conditions under which species signal and respond to need in favorable environments, and to quality in poor environments. The environmental conditions that favor this shift are widest when signalling costs and differences in quality between offspring are moderate. Low relatedness between siblings coupled with high signalling costs, as well as moderate relatedness between siblings coupled with low signalling costs, allow for the shift between signals of need and signals of quality to occur in more volatile environments. Further, only species whose offspring are highly dependent on parents for survival are not expected to use both signals of need and of quality. Ultimately, this shift between signalling need and signalling quality is the result of high-quality offspring benefiting more from meagre amounts of parental provisioning, while low-quality offspring have most to gain when parents can contribute more substantially. We show that this differential benefit of resources depends substantially upon offspring fitness as functions of parental investments, a variable which has lacked both diversity and biological realism in previous theoretical approaches. We then use this work to reassess previous theory on signals of need and of quality.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".