Exploring the role of temperature in the ocean through metabolic scaling
Bibliographic record
Abstract
Temperature imposes a constraint on the rates and outcomes of ecological processes that determine community- and ecosystem-level patterns. The application of metabolic scaling theory has advanced our understanding of the influence of temperature on pattern and process in marine communities. Metabolic scaling theory uses the fundamental and ubiquitous patterns of temperature-dependent metabolism to predict how environmental temperature influences patterns and processes at higher levels of biological organization. Here, we outline some of these predictions to review recent advances and illustrate how scaling theory might be applied to new challenges. For example, warming can alter species interactions and food-web structure and can also reduce total animal biomass supportable by a given amount of primary production by increasing animal metabolism and energetic demand. Additionally, within a species, larval development is faster in warmer water, potentially influencing dispersal and other demographic processes like population connectivity and gene flow. These predictions can be extended further to address major questions in marine ecology, and present an opportunity for conceptual unification of marine ecological research across levels of biological organization. Drawing on work by ecologists and oceanographers over the last century, a metabolic scaling approach represents a promising way forward for applying ecological understanding to basic questions as well as conservation challenges.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".