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The Causes of Resource Monopolization: Interaction Between Resource Dispersion and Mode of Competition

2004· article· en· W2015896478 on OpenAlexafffund
Laura K. Weir, James W. A. Grant

Bibliographic record

VenueEthology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsConcordia UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonopolizationCompetition (biology)Resource (disambiguation)ForagingConvictBiologyEcologyFisheryMicroeconomicsEconomicsGeographyMonopoly

Abstract

fetched live from OpenAlex

Abstract The degree of monopolization of resources is thought to be higher in groups that compete by interference than by exploitation. However, the monopolization of resources will presumably depend on (1) whether the dispersion of resources is economically defendable, and (2) whether some competitors have the ability to defend these resource distributions and hence capitalize on this potential. We tested for an interaction between the effects of temporal resource dispersion and aggressiveness on the degree of resource monopolization in a foraging system. Two species of fish differing markedly in aggressiveness (high: convict cichlids, Archocentrus nigrofasciatus ; low: goldfish, Carrasius auratus ) were allowed to compete intra‐specifically in groups of four for food that was either potentially defendable (arrived asynchronously) or not (arrived synchronously). As predicted, the monopolization of food, measured as the coefficient of variation of food eaten within groups, was significantly higher in the defendable than in the undefendable treatment for convict cichlids but not for goldfish. However, the monopolization of food was higher in the non‐aggressive goldfish than in the aggressive convict cichlids. Future studies should quantify and compare the monopolization in species that compete primarily via scramble competition to those that use primarily resource defence.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.095

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 head0.029
GPT teacher head0.284
Teacher spread0.254 · 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

Citations39
Published2004
Admission routes2
Has abstractyes

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