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Record W2800181570 · doi:10.1017/wet.2018.9

Rolled Winter Rye–Hairy Vetch Cover Crops for Weed Control in No-till Pumpkin

2018· article· en· W2800181570 on OpenAlexafffundabout
David Miville, Gilles Leroux

Bibliographic record

VenueWeed Technology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsAgronomyGlyphosateCover cropVicia villosaWeed controlBiologyWeedMulchBiomass (ecology)Legume

Abstract

fetched live from OpenAlex

Abstract Weed control is a challenging aspect of pumpkin production. Winter rye mulches may offer growers a means to manage weeds in pumpkin; however, rye degradation leads to an immobilization of soil nitrogen. Combining winter rye with a nitrogen fixing legume such as hairy vetch is an interesting option that may solve this problem. Twelve combinations including three hairy vetch seeding rates, two termination dates and the use or not of glyphosate before rolling cover crops were studied during the 2013 and 2014 growing seasons at the Laval University Agronomic Station in Saint-Augustin-de-Desmaures, Quebec, Canada to evaluate weed control and effects on pumpkin production. Adding hairy vetch to winter rye provided no benefits because of severe winterkill of the legume. Using glyphosate was necessary to prevent rye regrowth. Pumpkin growth was better and yields were higher than in the plots were no glyphosate was used. Mulches established at flowering (Zadoks 69) provided about 2,000 kg ha −1 more aboveground dry biomass than at early heading (Zadoks 51). This high biomass was essential in glyphosate treated plots in order to maintain excellent weed control throughout the growing season. When compared with the no-mulch weed-free control, yield in Zadoks 69+glyphosate treatment was lower in 2013 but comparable in 2014.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.232
Teacher spread0.219 · 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 designNot applicable
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

Citations15
Published2018
Admission routes3
Has abstractyes

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