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Record W4399049575 · doi:10.1186/s13059-024-03274-y

Current status of community resources and priorities for weed genomics research

2024· article· en· W4399049575 on OpenAlexaff
Jacob E. Montgomery, Sarah Morran, Dana R. MacGregor, J. Scott McElroy, Paul Neve, Célia Neto, Martín M. Vila‐Aiub, Maria Victoria Sandoval, Analía I. Menéndez, Julia M. Kreiner, Longjiang Fan, Ana L. Caicedo, Peter J. Maughan, Bianca Assis Barbosa Martins, Jagoda Mika, Alberto Collavo, Aldo Merotto, Nithya Subramanian, Muthukumar Bagavathiannan, Luan Cutti, Md Mazharul Islam, Bikram S. Gill, Robert M. Cicchillo, Roger E. Gast, Neeta Soni, Terry R. Wright, Gina Zastrow‐Hayes, Gregory D. May, Jenna Malone, Deepmala Sehgal, Shiv Shankhar Kaundun, Richard P. Dale, Juan Vorster, B. Peters, Jens Lerchl, Patrick J. Tranel, Roland Beffa, Alexandre Fournier‐Level, Mithila Jugulam, Kevin Fengler, Víctor Llaca, Eric L. Patterson, Todd A. Gaines

Bibliographic record

VenueGenome biology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Food and AgricultureCorteva AgriscienceFoundation for Food and Agriculture ResearchU.S. Department of AgricultureBayerNovo Nordisk Fonden
KeywordsBiologyGenomicsAdaptation (eye)BiotechnologyAgricultureWeedSelection (genetic algorithm)Environmental resource managementEcologyGenomeGeneticsComputer scienceGene

Abstract

fetched live from OpenAlex

Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

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)0.001
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 score0.814
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.113
GPT teacher head0.353
Teacher spread0.240 · 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

Citations36
Published2024
Admission routes1
Has abstractyes

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