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Record W4384934583 · doi:10.1101/2023.07.19.549613

The International Weed Genomics Consortium: Community Resources for Weed Genomics Research

2023· preprint· en· W4384934583 on OpenAlexaff
Jacob S. 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

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of British Columbia
FundersBiotechnology and Biological Sciences Research CouncilCorteva AgriscienceNational Institute of Food and AgricultureFoundation for Food and Agriculture ResearchBayerU.S. Department of Agriculture
KeywordsWeedGenomicsAgricultureWeed controlSelection (genetic algorithm)BiotechnologyAgroforestryBusinessEnvironmental resource managementBiologyComputer scienceEcologyGenomeEnvironmental scienceGene

Abstract

fetched live from OpenAlex

Abstract The International Weed Genomics Consortium is a collaborative group of researchers focused on developing genomic resources for the study of weedy plants. Weeds are attractive systems for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Our goal is to use genomic information to develop sustainable and effective weed control methods and to provide insights about biotic and abiotic stress tolerance to assist crop breeding. Here, we outline resources under development by the consortium and highlight areas of research that will be impacted by these enabling resources.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.977
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.076
GPT teacher head0.280
Teacher spread0.204 · 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.

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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

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