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THE ALLOMETRY OF PLANT SPACING THAT REGULATES FOOD INTAKE RATE IN MAMMALIAN HERBIVORES

2006· article· en· W1988769035 on OpenAlexaff
Daniel Fortin

Bibliographic record

VenueEcology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHerbivoreForagingAllometryEcologyMedicago sativaBiologyFunctional responseEcosystemScalingAgronomyPredationMathematics

Abstract

fetched live from OpenAlex

The distance that mammalian herbivores can travel without interrupting food processing corresponds to a distance threshold (d*) in plant spacing where change occurs in the mechanisms regulating the functional response. The instantaneous rate of food consumption is controlled by food encounter rate when plant spacing exceeds d*. Below this threshold, food processing in the mouth controls instantaneous intake rate. The distance threshold provides a mechanistic definition of the scale of heterogeneity of the food resource. Recent work indicates that d* should scale positively with the body mass of mammalian herbivores. Here I evaluated the empirical evidence for this positive scaling by investigating (1) herbivores consuming only alfalfa (Medicago sativa), (2) grazers, and (3) herbivores consuming any kind of vegetation. Overall, I found greater evidence for a negative than for a positive scaling of d*. Out of the three groups, only herbivores consuming alfalfa could yield a positive covariation between d* and body mass. However, even this positive scaling became negative when herbivores consumed bites of alfalfa that were only a fraction of their maximum size. Finally, d* also scaled negatively among herbivores foraging in similar food patches. Overall, differences in foraging decisions among mammalian herbivores seem more likely to have been shaped by a negative than a positive scaling of d*.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.190
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2006
Admission routes1
Has abstractyes

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