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Record W2156886141 · doi:10.1614/ws-d-11-00051.1

The Critical Period of Weed Control for Lentil in Western Canada

2011· article· en· W2156886141 on OpenAlexafffundabout
L. K. Fedoruk, Eric N. Johnson, Steven J. Shirtliffe

Bibliographic record

VenueWeed Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food CanadaBASF (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsAgronomyWeed controlWeedBiomass (ecology)Yield (engineering)CanopyCropCrop yieldBiologyEnvironmental scienceBotanyPhysics

Abstract

fetched live from OpenAlex

Weed control in lentil is difficult because lentil is a poor competitor with weeds and few POST broadleaf herbicides are available. Imadazolinone-tolerant lentils have more herbicide options, but the optimum timing for herbicide application is not known. The critical period of weed control (CPWC) is the period in a crop's life cycle when weeds must be controlled in order to prevent yield loss. The objective of this research was to determine the CPWC for lentil. We made lentil remain weedy or weed-free from 0 to 11 aboveground nodes to investigate the durations of weed interference and weed-free period, respectively. It was found that lentil has a CPWC beginning at the five-node stage and continuing to the 10-node stage. There was an inverse relationship between weed biomass and lentil yield; that is, lentil yield was highest when weed biomass is minimal. We propose that the CPWC begins when weeds start to accumulate significant biomass and ends with crop canopy closure. Therefore, to maximize lentil yields, growers should consider using a POST residual herbicide that can control weeds during the CPWC.

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 score0.059
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.025
GPT teacher head0.234
Teacher spread0.209 · 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

Citations22
Published2011
Admission routes3
Has abstractyes

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