MétaCan
Menu
Back to cohort
Record W2001957949 · doi:10.1007/s12080-010-0094-9

Starve a competitor: evolution of luxury consumption as a competitive strategy

2010· article· en· W2001957949 on OpenAlexaff
Claire de Mazancourt, Mark W. Schwartz

Bibliographic record

VenueTheoretical Ecology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcGill University
FundersNatural Environment Research Council
KeywordsConsumption (sociology)Competition (biology)Resource (disambiguation)Outcome (game theory)PopulationSelection (genetic algorithm)LimitingProcess (computing)Function (biology)EconomicsMicroeconomicsEcologyBiologyComputer scienceEvolutionary biology

Abstract

fetched live from OpenAlex

Organisms are often observed to acquire an excess of non-limiting resources, a process known as luxury consumption. Luxury consumption has been largely treated as a bet hedging strategy for temporal variation in resource supply, but may also function as a competitive strategy. We incorporate luxury resource consumption into a derivation of the classic resource ratio model for competition between terrestrial plant, and explore its consequences for population dynamics and competition. We show that luxury consumption reduces the potential for coexistence between two species competing for two resources. Furthermore, we demonstrate that luxury consumption can be selected for because of the competitive advantage that luxury consumers gain. Luxury consumption evolves when competition for resources is local rather than global, there is potential for coexistence between the two species and the competitive environment remains stable over a sufficient period of time to allow selection to act. The evolutionary outcome can be either extinction of one of the competing species or coexistence of the two species with maximum luxury consumption. The potential for selection to favor luxury consumption is well predicted by the competitive outcome between individuals of the two species with and without luxury consumption.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.996

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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 designTheoretical or conceptual
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

Citations48
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueTheoretical EcologySame topicPlant and animal studiesFrench-language works237,207