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Record W4403601386 · doi:10.1002/agj2.21709

Weed management in organic dryland wheat production in the Pacific Northwest

2024· article· en· W4403601386 on OpenAlexaboutno aff
Ian C. Burke, Nicole E. Tautges, Misha R. Manuchehri

Bibliographic record

VenueAgronomy Journal · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
FundersNational Institute of Food and Agriculture
KeywordsAgronomyWeedWeed controlEnvironmental scienceProduction (economics)Dryland farmingAgroforestryWinter wheatBiologyAgricultureEcologyEconomics

Abstract

fetched live from OpenAlex

Abstract Growth in demand for organic small grains has increased interest in producing certified organic crops in the semiarid US Pacific Northwest. The region is well‐suited for small grain production, and there is a strong market for organic food products on the US West Coast. However, many growers encounter significant and persistent challenges with weed management, particularly management of perennial weeds such as Canada thistle [ Cirsium arvense (L.) Scop.] and field bindweed ( Convolvulus arvensis L.), but also common winter and spring annual grass weeds including cheatgrass ( Bromus tectorum L.) and wild oat ( Avena fatua L.). Coupled with the need to minimize soil disturbance, weed management can become nearly intractable and production limiting. From 2004 to 2024, several short and intermediate studies have been conducted to assess weed control tactics and crop rotation effects on weed management. Lessons learned include incorporating alfalfa ( Medicago sativa L.) and spring barley ( Hordeum vulgare L.) into rotations for suppression of field bindweed, or alfalfa and winter triticale (x Triticosecale Wittmack) for suppression of Canada thistle. Optimization of cultural inputs, particularly seeding rate, are critical for each crop in rotation. Animal integration and new crops such as quinoa ( Chenopodium quinoa Willd.) are alternatives to conventional crops and potentially profitable. Incorporation of precision mechanical and chemical systems is feasible in narrow‐row cereals, and when combined with crop rotation, it could reduce or eliminate the need for repeated transitions back to convention production for organic growers.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.412

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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 designOther design
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

Citations4
Published2024
Admission routes1
Has abstractyes

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