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Record W2170831380 · doi:10.1126/science.1182213

Adaptive Evolution of Pelvic Reduction in Sticklebacks by Recurrent Deletion of a <i>Pitx1</i> Enhancer

2009· article· en· W2170831380 on OpenAlexaff
Yingguang Frank Chan, Melissa E. Marks, Felicity C. Jones, Guadalupe Villarreal, Michael D. Shapiro, Shannon D. Brady, Audrey M. Southwick, Devin Absher, Jane Grimwood, Jeremy Schmutz, R Myers, Dmitri A. Petrov, Bjarni Jónsson, Dolph Schluter, Michael A. Bell, David M. Kingsley

Bibliographic record

VenueScience · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsUniversity of British Columbia
FundersNational Human Genome Research InstituteHoward Hughes Medical Institute
KeywordsBiologyEnhancerGeneticsHomeoboxPhenotypeGeneLocus (genetics)AlleleTranscription factor

Abstract

fetched live from OpenAlex

The molecular mechanisms underlying major phenotypic changes that have evolved repeatedly in nature are generally unknown. Pelvic loss in different natural populations of threespine stickleback fish has occurred through regulatory mutations deleting a tissue-specific enhancer of the Pituitary homeobox transcription factor 1 (Pitx1) gene. The high prevalence of deletion mutations at Pitx1 may be influenced by inherent structural features of the locus. Although Pitx1 null mutations are lethal in laboratory animals, Pitx1 regulatory mutations show molecular signatures of positive selection in pelvic-reduced populations. These studies illustrate how major expression and morphological changes can arise from single mutational leaps in natural populations, producing new adaptive alleles via recurrent regulatory alterations in a key developmental control gene.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.262
Teacher spread0.255 · 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 designBench or experimental
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

Citations1,063
Published2009
Admission routes1
Has abstractyes

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