A broader interpretation of energy landscapes
Bibliographic record
Abstract
The energy landscapes paradigm describes how spatial variation in energetic costs of transport (locomotion) influences animal movement. We suggest a more holistic view of the energetic costs and gains that vary across landscapes. Firstly, the spatial distribution of potential energetic gain is a major factor in animal spatial ecology. In addition to food availability, metabolic performance determines the capacity of animals to capture and digest food, which can vary dramatically across space and time. Independent of movement, energetic costs and gains vary spatially with environmental factors like temperature. We therefore consider energy landscapes more broadly as the variation in animal energetic costs and gains over space and time. This is discussed conceptually, where we posit testable hypotheses on how factors like prey, predators, and temperature interact to affect animal energetics. We illustrate these ideas with empirical data on a marine fish in multiple landscapes showing variable patterns in energetic costs and potential gain due to their spatial ecology. The broader definition of ‘energy landscapes’ we propose provides a comprehensive framework for understanding animal spatial ecology, as well as the effects of changing environmental conditions on animal fitness, life history, and population dynamics in the spatial domain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".