Physiological Performance Curves: When Are They Useful?
Bibliographic record
Abstract
This review serves as an introduction to a special issue of Frontiers in Physiology, focused on the importance of physiological performance curves across phylogenetic and functional boundaries. Biologists have used performance curves to describe the effects of changing environmental conditions on animal physiology since the late 1800s (at least). Animal physiologists have studied performance curves extensively over the past decades, and there is a good foundation to understanding how the environment affects physiological functions of individuals. Our goal here was to build upon this research and address outstanding questions regarding the mutability and applicability of performance curves across taxonomic groups and levels of biological organization. Performance curves are not fixed at a taxonomic, population, or individual level - rather they are dynamic and can shift in response to evolutionary pressures (e.g., selection) and epigenetic programming (e.g., plasticity). The mechanisms underlying these shifts are being increasingly used to predict the efficacy with which plasticity and heritability of performance curves can render individuals and populations less vulnerable to climate change. Individual differences in physiological performance curves (and plasticity of performance curves) can also have cascading effects at higher levels of biological organization. For instance, individual physiology likely influences group behaviors in non-additive ways. There is a need therefore to extend the concept of performance curves to social interactions and sociality. Collectively, this special issue emphasizes the power of how within- and between-individual shifts in performance curves might scale up to the population-, species-, and community-level dynamics that inform conservation management strategies.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".