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Record W2685334428 · doi:10.1126/science.aao0960

Natural selection shaped the rise and fall of passenger pigeon genomic diversity

2017· article· en· W2685334428 on OpenAlexaff
Gemma G. R. Murray, André E. R. Soares, Ben J. Novak, Nathan K. Schaefer, James A. Cahill, Allan J. Baker, John R. Demboski, Andrew Doll, Rute R. da Fonseca, Tara L. Fulton, M. Thomas P. Gilbert, Peter D. Heintzman, Brandon Letts, George C. McIntosh, Brendan L. O’Connell, Mark Peck, Marie-Lorraine Pipes, Edward S. Rice, Kathryn M. Santos, A. Gregory Sohrweide, Samuel H. Vohr, Russell Corbett‐Detig, Richard E. Green, Beth Shapiro

Bibliographic record

VenueScience · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsEnvironment and Climate Change CanadaAlberta Environment and Protected AreasRoyal Ontario Museum
FundersNational Center for Research ResourcesVillum FondenGordon and Betty Moore FoundationDavid and Lucile Packard Foundation
KeywordsSelection (genetic algorithm)Diversity (politics)Natural selectionBiologyEvolutionary biologyGenomic selectionGeneticsComputer sciencePolitical scienceArtificial intelligenceGeneGenotype

Abstract

fetched live from OpenAlex

The extinct passenger pigeon was once the most abundant bird in North America, and possibly the world. Although theory predicts that large populations will be more genetically diverse, passenger pigeon genetic diversity was surprisingly low. To investigate this disconnect, we analyzed 41 mitochondrial and 4 nuclear genomes from passenger pigeons and 2 genomes from band-tailed pigeons, which are passenger pigeons' closest living relatives. Passenger pigeons' large population size appears to have allowed for faster adaptive evolution and removal of harmful mutations, driving a huge loss in their neutral genetic diversity. These results demonstrate the effect that selection can have on a vertebrate genome and contradict results that suggested that population instability contributed to this species's surprisingly rapid extinction.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations135
Published2017
Admission routes1
Has abstractyes

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