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Record W2533186084 · doi:10.1139/cjps-2016-0183

Seed shatter of six economically important weed species in producer fields in Saskatchewan

2016· article· en· W2533186084 on OpenAlexafffundvenueabout
Nikki Burton, Hugh J. Beckie, Christian J. Willenborg, Steven J. Shirtliffe, J.J. Schoenau, Eric N. Johnson

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaCollege of Agriculture and Bioresources, University of Saskatchewan
KeywordsAvena fatuaBiologyConvolvulusWeedCanolaFoxtailAgronomyGaliumSetaria viridisPopulationChenopodiumWeed controlBrassicaSetariaCirsium arvenseHorticulture

Abstract

fetched live from OpenAlex

Seed shatter of wild oat (Avena fatua L.), green foxtail [Setaria viridis (L.) P. Beauv.], wild mustard (Sinapis arvensis L.), cleavers (Galium spurium L. and G. aparine L.), wild buckwheat (Polygonum convolvulus L.), and kochia [Kochia scoparia (L.) Schrad.] was evaluated in field pea, spring wheat, and canola fields in Saskatchewan in 2014 and 2015. Seed shatter was assessed using shatter trays collected once a week during the crop ripening stage, as well as at swathing or direct-harvest (direct-combining). Seed shatter differed among weed species in field pea and wheat at maturity: 22%–30% for wild oat, and generally ≤10% for the other species. Seed shatter of investigated weeds in canola at swathing, including that of wild oat, was uniformly low (<5%). The relatively low level of seed shatter for cleavers, wild mustard, green foxtail, and wild buckwheat suggests that these species may be suitable candidates for harvest weed seed control (HWSC). Because of the amount and timing of wild oat seed shatter, HWSC may not reduce population abundance of this grassy weed, except in canola when swathed.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.971

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 head0.012
GPT teacher head0.184
Teacher spread0.171 · 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 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

Citations25
Published2016
Admission routes4
Has abstractyes

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