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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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; both teacher heads agree on what is shown here.
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".