Bibliographic record
Abstract
While ecologists sometimes bemoan the complexities of the discipline, several of the overarching patterns of nature can be boiled down to surprisingly simple terms. One of the most intriguing of these is Damuth’s law (1) that population density is scaled with body mass raised to the power of −3/4 irrespective of taxa or time. Because metabolic demand is allometrically scaled in identical fashion, a local population of 5-kg hares requires essentially the same energy to sustain itself as a herd of 50-kg deer. This implies that different species of organisms are energetically equivalent and hence interchangeable in a very basic sense. On the other hand, reconstructions of mammalian lineages consistently demonstrate gradual replacement of small species with larger versions over evolutionary time. This pattern, known as Cope’s rule (2), suggests that fundamental selective advantages accrue with increase in body size, seemingly at odds with Damuth’s law. If large animals are no more energetically efficient than their smaller brethren, why would one expect any directional trajectory over evolutionary time? This paradox is neatly explained by Bhat et al.’s paper (3) in PNAS. Linking a body of metabolic theory championed by J. H. Brown et al. (4) with fractal-based conjectures (5, 6) about the degree of patchiness in food supply experienced by animals of different size, Bhat et al. (3) use energetic cost−benefit analysis to construct a minimalist model of mammalian life-history constraints. Because the process of gathering food items is inherently stochastic, whereas metabolic costs of foraging are continuous, the energy state of each individual gradually drifts upward or downward over time, much like wind-borne grains of pollen (2). This diffusive process occasionally results in a lucky individual acquiring sufficient energy to facilitate a reproductive event, at which point the energy balance is reset to a new lower level, much … [↵][1]1Email: jfryxell{at}uoguelph.ca. [1]: #xref-corresp-1-1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".