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Patterns in body mass distributions: sifting among alternative hypotheses

2006· article· en· W2138043962 on OpenAlexaff
CHARLES R. ALLEN, Ahjond S. Garmestani, Tanya D. Havlicek, Pablo A. Marquet, Garry Peterson, Carla Restrepo, Craig A. Stow, B. E. Weeks

Bibliographic record

VenueEcology Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEcologyAllometryBiologyTemporal scalesBiosphereRelevance (law)Spatial ecology

Abstract

fetched live from OpenAlex

Understanding how animals interact with their environment is critical for evaluating, mitigating and coping with anthropogenic alteration of Earth's biosphere. Researchers have attempted to understand some aspects of these interactions by examining patterns in animal body mass distributions. Energetic, phylogenetic, biogeographical, textural discontinuity and community interaction hypotheses have been advanced to explain observed patterns. Energetic and textural discontinuity hypotheses focus upon the allometry of resource use. The community interaction hypothesis contends that biotic interactions within assemblages of species are of primary importance. Biogeographical and phylogenetic hypotheses focus on the role of constraints on the organization of communities. This paper examines and organizes these various propositions about species body mass distributions and discusses the multiple competing hypotheses, how their predictions vary, and possible methods by which the hypotheses can be distinguished and tested. Each of the hypotheses is partial, and explains some elements of pattern in body mass distributions. The scale of appropriate application, relevance and interpretation varies among the hypotheses, and the mechanisms underlying observed patterns are likely to be multicausal and vary with scale.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.207
Teacher spread0.202 · 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 teacher head, 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

Citations175
Published2006
Admission routes1
Has abstractyes

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