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Record W2803514223 · doi:10.1073/pnas.1716257115

Selection and environmental adaptation along a path to speciation in the Tibetan frog <i>Nanorana parkeri</i>

2018· article· en· W2803514223 on OpenAlexaff
Guodong Wang, Baolin Zhang, Weiwei Zhou, Yongxin Li, Jie‐Qiong Jin, Yong Shao, Hechuan Yang, Liu Yan-hu, Yan Fang, Hong-Man Chen, Li Jin, Feng Gao, Yaoguang Zhang, Haipeng Li, Bingyu Mao, Robert W. Murphy, David B. Wake, Ya‐Ping Zhang, Jing Che

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsRoyal Ontario Museum
FundersNational Natural Science Foundation of China
KeywordsGenetic algorithmBiologyAdaptation (eye)Natural selectionEvolutionary biologyIncipient speciationPopulation genomicsSelection (genetic algorithm)Reproductive isolationEcological speciationPopulationEcologyGenomicsGenomeGenetic variationGene flowGeneticsComputer scienceGene

Abstract

fetched live from OpenAlex

Significance Central topics in evolutionary biology include uncovering the processes and genetic bases of speciation and documenting environmental adaptations and processes responsible for them. The challenging environment of the Qinghai-Tibetan Plateau (QTP) facilitates such investigations, and the Tibetan frog, Nanorana parkeri , offers a unique opportunity to investigate these processes. A cohort of whole-genome sequences of 63 individuals from across its entire range opens avenues for incorporating population genomics into studies of speciation. Natural selection plays an important role in maintaining and driving the continuing divergence and reproductive isolation of populations of the species. The QTP is a natural laboratory for studying how selection drives adaptation, how environments influence evolutionary history, and how these factors can interact to provide insight into speciation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.107

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 studies0.0000.000
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 head0.020
GPT teacher head0.249
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 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

Citations70
Published2018
Admission routes1
Has abstractyes

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