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Record W4206821415 · doi:10.3410/f.13947956.15403056

Faculty Opinions recommendation of Spite and the scale of competition in Pseudomonas aeruginosa.

2012· dataset· en· W4206821415 on OpenAlexaff
Benjamin M. Bolker

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2012
Typedataset
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCompetition (biology)Altruism (biology)Pseudomonas aeruginosaScale (ratio)HarmBiologySelection (genetic algorithm)PsychologySocial psychologyGeneticsComputer scienceEcologyBacteriaGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Scale of competition has been shown to be an important factor in shaping the evolution of social interactions. Although many theoretical and experimental studies have examined its effect on altruistic cooperation, relatively little research effort has been focused on spiteful behaviors--actions that harm both the actor and the recipient. In this study, we expand on existing theory by investigating the importance of the global frequency of spiteful alleles, and we determine experimentally how the scale of competition affects selection for spite in the bacterial pathogen Pseudomonas aeruginosa under high and intermediate spatial relatedness. Consistent with our theoretical results, we found in our experiments that spiteful genotypes are more favored under local (rather than global) competition and intermediate (rather than high) spatial relatedness, conditions that have been shown to select against indiscriminate altruism. PMID: 21750390

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)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0560.055

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 head0.025
GPT teacher head0.349
Teacher spread0.325 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

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