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THE IMPACT OF HABITAT SELECTION ON THE SPATIAL HETEROGENEITY OF RESOURCES IN VARYING ENVIRONMENTS

2000· article· en· W2087349806 on OpenAlexaff
Peter A. Abrams

Bibliographic record

VenueEcology · 2000
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPredationEcologySpatial heterogeneityHabitatResource (disambiguation)PopulationSelection (genetic algorithm)LagOverexploitationBiologyDemographyComputer science

Abstract

fetched live from OpenAlex

This article used a series of simple models to examine the population dynamics of two independently reproducing resource populations in spatially distinct patches, when the resources are eaten by a fixed consumer population that moves adaptively between the patches. The models assumed that each resource (patch) experienced a potentially different set of environmental influences on its growth rate. The models also assumed that consumer distribution shifted more rapidly toward the more rewarding patch as the difference in resource intake rates between the patches increased. In most cases, the time-average of spatial heterogeneity of resource densities is greatest for intermediate rates of consumer movement. The average resource densities and consumer fitness are also frequently maximal when movement rates are intermediate. Intermediate movement rates allow enough of a lag in consumer redistribution that resources periodically escape overexploitation. Resource densities and heterogeneity may increase by a large factor as the rate of consumer movement (or the sensitivity of consumer movement to differences in intake rates) increases. These findings were used to discuss the potential effects of predators that interfere with habitat selection by their prey. Such predators may have positive or negative effects on the density of their prey and of their prey's resources. Predators may also either increase or decrease the spatial heterogeneity of resource densities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.291
Teacher spread0.272 · 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.

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

Citations71
Published2000
Admission routes1
Has abstractyes

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