Bibliographic record
Abstract
Ecological theory that is grounded in metabolic currencies and constraints offers the potential to link ecological outcomes to biophysical processes across multiple scales of organization. The metabolic theory of ecology (MTE) has emphasized the potential for metabolism to serve as a unified theory of ecology, while focusing primarily on the size and temperature dependence of whole-organism metabolic rates. Generalizing metabolic ecology requires extending beyond prediction and application of standardized metabolic rates to theory focused on how energy moves through ecological systems. A bibliometric and network analysis of recent metabolic ecology literature reveals a research network characterized by major clusters focused on MTE, foraging theory, bioenergetics, trophic status, and generalized patterns and predictions. This generalized research network, which we refer to as metabolic ecology, can be considered to include the scaling, temperature and stoichiometric models forming the core of MTE, as well as bioenergetic equations, foraging theory, life-history allocation models, consumer-resource equations, food web theory and energy-based macroecology models that are frequently employed in ecological literature. We conclude with six points we believe to be important to the advancement and integration of metabolic ecology, including nomination of a second fundamental equation, complementary to the first fundamental equation offered by the MTE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".