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Record W3161653107 · doi:10.1038/s41467-021-22858-x

The tepary bean genome provides insight into evolution and domestication under heat stress

2021· article· en· W3161653107 on OpenAlexafffund
Samira Mafi Moghaddam, Atena Oladzad, ChuShin Koh, Larissa Ramsay, John P. Hart, Sujan Mamidi, Genevieve M. Hoopes, Avinash Sreedasyam, Andrew T. Wiersma, Dongyan Zhao, Jane Grimwood, John P. Hamilton, Jerry Jenkins, Brieanne Vaillancourt, Joshua C. Wood, Jeremy Schmutz, Sateesh Kagale, Timothy G. Porch, Kirstin E. Bett, C. Robin Buell, Phillip E. McClean

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsNational Research Council CanadaSaskatchewan Research Council (Canada)Global Institute for Water SecurityUniversity of Saskatchewan
FundersAgricultural Research ServiceU.S. Department of AgricultureCollege of Engineering, Michigan State UniversitySaskatchewan Pulse GrowersUnited States Agency for International DevelopmentMichigan State University
KeywordsPhaseolusBiologyDomesticationLegumeAdaptation (eye)GenomeGeneAgronomyBiotechnologyGenetics

Abstract

fetched live from OpenAlex

Tepary bean (Phaseolus acutifolis A. Gray), native to the Sonoran Desert, is highly adapted to heat and drought. It is a sister species of common bean (Phaseolus vulgaris L.), the most important legume protein source for direct human consumption, and whose production is threatened by climate change. Here, we report on the tepary genome including exploration of possible mechanisms for resilience to moderate heat stress and a reduced disease resistance gene repertoire, consistent with adaptation to arid and hot environments. Extensive collinearity and shared gene content among these Phaseolus species will facilitate engineering climate adaptation in common bean, a key food security crop, and accelerate tepary bean improvement.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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 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

Citations93
Published2021
Admission routes2
Has abstractyes

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Same venueNature CommunicationsSame topicPlant pathogens and resistance mechanismsFrench-language works237,207