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Record W2774432807 · doi:10.1371/journal.pone.0189124

Co-existence of multiple trade-off currencies shapes evolutionary outcomes

2017· article· en· W2774432807 on OpenAlexafffund
Alan A. Cohen, Caroline Isaksson, Roberto Salguero‐Gómez

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de Sherbrooke
FundersVetenskapsrådetNatural Environment Research CouncilCanadian Institutes of Health ResearchEuropean CommissionAustralian Research CouncilSight Research UK
KeywordsCurrencyTrade-offReproductionEconomicsEvolutionary biologyContingencyDigital currencyBiologySurvival of the fittestMonetary economicsEcology

Abstract

fetched live from OpenAlex

Evolutionary studies often assume that energy is the primary resource (i.e. "currency") at the heart of the survival-reproduction trade-off, despite recent evidence to the contrary. The evolutionary consequences of having a single trade-off currency versus multiple competing currencies are unknown. Using simulations, we modeled the evolution of either a single physiological currency between reproduction and survival, or of multiple such currencies. For a wide array of model specifications varying functional forms and strengths of the trade-offs, we show that the presence of multiple currencies (e.g. nutrients, time) generally results in the evolution of higher lifetime reproductive success through partial circumvention of such trade-offs. Evolution of the underlying physiology is also more highly contingent with multiple currencies. These results challenge the paradigm of a single survival-reproduction trade-off as central to life history evolution, suggesting greater roles for physiological constraints and contingency, and implying potential selection for evolution of multiple trade-off currencies.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.120
GPT teacher head0.271
Teacher spread0.151 · 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 designSimulation or modeling
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

Citations27
Published2017
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicAnimal Behavior and ReproductionFrench-language works237,207