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Record W2902340823 · doi:10.1139/cjps-2018-0165

Determination of optimal sowing dates and densities of winter wheat under Quebec growing conditions

2018· article· en· W2902340823 on OpenAlexaffvenueabout
Francis Allard, Anne Vanasse, Denis Pageau, Gilles Tremblay, Julie Durand, Elizabeth Vachon

Bibliographic record

VenueCanadian Journal of Plant Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food CanadaInstitut national de psychiatrie légale Philippe-PinelUniversité LavalMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
Fundersnot available
KeywordsSowingSeedingYield (engineering)AgronomyCultivarWinter wheatBiologySemis

Abstract

fetched live from OpenAlex

The objective of this project was to determine the optimal sowing dates and densities for winter wheat to increase winter survival and yield under Quebec growing conditions. The trials were carried out from 2014 to 2016 at four sites, representing three cereal production zones (zones 1, 2, and 3). Three cultivars were assessed using four sowing dates and four seeding densities (250, 350, 450, and 550 seeds m −2 ). In the first year, the wheat at two of the four sites survived (82%–100%), and in the second year, all the sites showed good survival rates (69%–99%). In zone 2, winter survival was higher for the early sowing dates compared with later dates. Sowing date and seeding density had no effect on survival in zones 1 and 3. Maximum yields were attained with sowing dates from mid- to late September in zone 1; from early to mid-September in zone 2; and from mid-August to mid-September in zone 3. An increase in seeding density from 250 to 550 seeds m −2 led to an average yield gain of 9% in zones 2 and 3, but no gain in zone 1. Winter survival rates and yield differ between cultivars.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.995

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 studies0.0000.001
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 head0.021
GPT teacher head0.226
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 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

Citations7
Published2018
Admission routes3
Has abstractyes

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