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Record W2110913876 · doi:10.1098/rsbl.2006.0464

Non-omnivorous generality promotes population stability

2006· article· en· W2110913876 on OpenAlexafffund
Tamara N. Romanuk, Beatrix E. Beisner, Neo D. Martinez, Jurek Kolasa

Bibliographic record

VenueBiology Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMcMaster UniversityUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsBiologyGeneralityOmnivorePopulationStability (learning theory)Evolutionary biologyEcologyZoologyPredationDemographyComputer science

Abstract

fetched live from OpenAlex

Using a combination of stable isotope analysis of delta13C and delta15N and long-term census data on population abundances for meiofauna in tropical aquatic rock pools, we provide evidence that species which exhibit greater variation in delta13C, an indication of a greater range of distinct carbon sources in their diet, have more stable populations than species with lower variation in delta13C. This link between increased isotope variability and reduced population variability, however, did not hold for delta15N. This suggests that increases in population stability were due to non-omnivorous feeding on multiple carbon sources within a trophic level rather than omnivorous feeding on multiple carbon sources across trophic levels. Our findings corroborate MacArthur's original hypothesis that populations that can access a greater range of resources are more stable than those which consume a more restricted range of resources.

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)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations13
Published2006
Admission routes2
Has abstractyes

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