MetaR, a global database on metabolic rates of ectotherms
Bibliographic record
Abstract
Whole-organism metabolic rate is a key trait for understanding ectotherms’ responses to ongoing environmental change. It represents the interface through which organisms interact with their environment and therefore allows for making predictions across various levels of biological organisation. While much of the variation in metabolic rates is explained by body size and temperature, a considerable part of this variation remains unexplained. Lack of standard research practices, data sparsity and insufficient coverage of various taxa limit our capacity to conduct a meaningful synthesis across the Tree of Life; both in the spatial and the temporal dimension. To overcome these limitations, and acquire a better understanding of the evolution of metabolic rates, we created MetaR: which is to date the most comprehensive database on intra- and interspecific variations in ectotherms’ metabolic rates. This database currently comprises over 95,000 records covering more than 2,400 species across 16 phyla of ectotherms, encompassing both invertebrates and small and large vertebrates from marine, intertidal, freshwater and terrestrial ecosystems. MetaR also integrates methodological details, which further improves our capacity, on the one hand, to detect ecological, physiological and evolutionary patterns and, on the other hand, to forecast the functional impacts of global environmental changes across the Tree of Life from a metabolic perspective
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".