Linking ecomechanics and ecophysiology to interspecific interactions and community dynamics
Bibliographic record
Abstract
To predict community-level responses to climate change, we must understand how variation in environmental conditions drives changes in an organism's ability to acquire resources and translate those resources into growth, reproduction, and survival. This challenge can be approached mechanistically by establishing linkages from biophysics to community ecology. For example, body temperature can be predicted from environmental conditions and species-specific morphological and behavioral traits. Variation in body temperature within and among species dictates physiological performance, rates of resource acquisition, and growth. These ecological characteristics, along with population size, define the strength with which species interact. Finally, the direct (individual level) and indirect (community level) effects of temperature jointly determine community structure. This mechanistic framework can complement correlational approaches to better predict ecological responses to climate change and identify which characteristics of a species or community act as leverage points for change. Research priorities for further development of the mechanistic approach include documentation and prediction of relevant spatial and temporal variation in body temperature and the relationships between body temperature, individual performance, and interspecific interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".