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Record W6948713548 · doi:10.5061/dryad.8b5n18p

Data from: Environmental stress does not increase the mean strength of selection

2018· dataset· en· W6948713548 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSelection (genetic algorithm)IntuitionGenetic FitnessGenetic variationGenetic loadGroup selectionNatural selection

Abstract

fetched live from OpenAlex

A common intuition among evolutionary biologists and ecologists is that environmental stress will increase the strength of selection against deleterious alleles and among alternate genotypes. However, the strength of selection is determined by the relative fitness differences among genotypes, and there is no theoretical reason why these differences should be exaggerated as mean fitness decreases. We update a recent review of the empirical results pertaining to environmental stress and the strength of selection and find that there is no overall trend towards increased selection under stress. The majority of past studies measure the strength of selection by quantifying the decrease in fitness imposed by single or multiple mutations in different environments. However, selection rarely acts on one locus independently, and the strength of selection will be determined by variation across the whole genome. We used 20 inbred lines of Drosophila melanogaster to make repeated fitness measurements of the same genotypes in four different environments. This framework allowed us to determine the variation in fitness attributable to genotype across stressful environments and to calculate the opportunity for selection among these genotypes in each stress. While we found significant decreases in mean fitness in our stressful environments, we did not find any significant differences in the strength of selection among any of the four measured environments. Therefore, in agreement with our updated review, we find no evidence for the oft-cited verbal model that stress increases the strength of selection.

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 categoriesMeta-epidemiology (narrow)
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 score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.001
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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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 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
Published2018
Admission routes1
Has abstractyes

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