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Record W3041313107 · doi:10.3389/fevo.2020.00216

Fitness Estimation for Ecological Studies: An Evaluation in Columbian Ground Squirrels

2020· article· en· W3041313107 on OpenAlexafffund
F. Stephen Dobson, Jan O. Murie, Vincent A. Viblanc

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de StrasbourgMinistry of EnvironmentAuburn UniversityFondation FyssenCentre National de la Recherche ScientifiqueAXA Research FundUniversity of CalgaryNational Science Foundation
KeywordsEcologyNatural selectionSelection (genetic algorithm)Reproductive successGenetic FitnessInclusive fitnessEstimationBiologyEvolutionary ecologyComputer scienceDemographyMachine learningPopulationEngineering

Abstract

fetched live from OpenAlex

The topics of evolution by natural selection and ecological interactions are closely intertwined. Thus, measurements of evolutionary fitness are ubiquitous in the ecological literature. As an empirical problem, the components of fitness, reproduction and survival, may be analyzed to produce fitness estimates in several ways. These can be divided into annual estimates that are most appropriate for short-term (e.g., annual) and experimental studies, and lifetime fitness estimates that are most appropriate for evaluating functional organismal traits. The latter are appropriate for comparative studies¬¬ of natural selection in species with lifetimes that extend over several years. These estimates may also be particularly useful for estimating the direct and indirect components of inclusive fitness, an important topic for the evolution of cooperation. We present examples of some of these alternatives from our research on Columbian ground squirrels (Urocitellus columbianus). Using empirical data, we also test the degree of correspondence of annual fitness, lifetime reproductive success, and individual fitness measures that are based on matrix methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.417

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)0.0000.000

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 head0.052
GPT teacher head0.310
Teacher spread0.257 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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