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Record W2321899709 · doi:10.1139/cjps-2014-439

Seeding rate and cultivar effects on yield, yield components and grain quality of spring spelt in eastern Canada

2015· article· en· W2321899709 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsGrain Research CentreAgriculture and Agri-Food CanadaUniversité Laval
Fundersnot available
KeywordsCultivarAgronomySeedingStrawYield (engineering)Grain qualityGrain yieldTest weightSpring (device)BiologyCropMathematicsMaterials science

Abstract

fetched live from OpenAlex

Dorval, I., Vanasse, A., Pageau, D. and Dion, Y. 2015. Seeding rate and cultivar effects on yield, yield components and grain quality of spring spelt in eastern Canada. Can. J. Plant. Sci. 95: 841-849. There is currently an increasing demand from master millers for spelt (Triticum aestivum ssp. spelta), but little is known about crop management of spring spelt under the eastern Canadian climate in organic or low-input systems. Field experiments were carried out at three sites in Quebec from 2011 to 2013 to assess the effect of cultivar (CDC Origin, CDC Zorba, CDC Nexon, CDC Silex) and seeding rate (250, 300, 350, 400 and 450 grains m-2) on grain and straw yields, yield components and some grain quality characteristics of spelt. CDC Origin produced higher hulled grain yields at all sites, while CDC Silex produced similar hulled grain yields and achieved the highest naked grain yields at two of the three test sites. The hull content varied from 24.0 to 37.6% among cultivars. CDC Origin had the highest hull content at each site. The seeding rate generally had no effect on yields, but had an effect on yield components; increasing seeding rates slightly increased the number of spikes per square metre and decreased the number of grains per spike, while the 1000-grain weight remained unaffected. Protein content of all cultivars was high (14.2 to 15.4%), while falling number varied from 219 to 385 s.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.732

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)0.0000.000

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 head0.431
GPT teacher head0.256
Teacher spread0.175 · 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