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Record W6929765342 · doi:10.5061/dryad.3ps4r

Data from: Flight loss linked to faster molecular evolution in insects

2013· dataset· en· W6929765342 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2013
Typedataset
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInsect flightEctothermBiological dispersalPopulationMolecular evolutionMitochondrial DNARange (aeronautics)

Abstract

fetched live from OpenAlex

The loss of flight ability has occurred thousands of times independently during insect evolution. Flight loss may be linked to higher molecular evolutionary rates because of reductions in effective population sizes (Ne) and relaxed selective constraints. Reduced dispersal ability increases population subdivision, may decrease geographical range size and increases (sub)population extinction risk, thus leading to an expected reduction in Ne. Additionally, flight loss in birds has been linked to higher molecular rates of energy-related genes, probably owing to relaxed selective constraints on energy metabolism. We tested for an association between insect flight loss and molecular rates through comparative analysis in 49 phylogenetically independent transitions spanning multiple taxa, including moths, flies, beetles, mayflies, stick insects, stoneflies, scorpionflies and caddisflies, using available nuclear and mitochondrial protein-coding DNA sequences. We estimated the rate of molecular evolution of flightless (FL) and related flight-capable lineages by ratios of non-synonymous-to-synonymous substitutions (dN/dS) and overall substitution rates (OSRs). Across multiple instances of flight loss, we show a significant pattern of higher dN/dS ratios and OSRs in FL lineages in mitochondrial but not nuclear genes. These patterns may be explained by relaxed selective constraints in FL ectotherms relating to energy metabolism, possibly in combination with reduced Ne.

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 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 score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.003
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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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