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Record W4214914606 · doi:10.1139/cjps-2021-0235

A comparison of soybean maturity groups for phenology, seed yield, and seed quality components between eastern Ontario and southern Manitoba

2022· article· en· W4214914606 on OpenAlexafffundvenueabout
Nathaniel W. W. Ort, Malcolm J. Morrison, Elroy R. Cober, Darryl J. McAndrew, Yvonne Lawley

Bibliographic record

VenueCanadian Journal of Plant Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
FundersAgriculture and Agri-Food CanadaWestern Grains Research Foundation
KeywordsPhenologyBiologyCultivarYield (engineering)photoperiodismAgronomyPrecipitationGrowing degree-dayVegetative reproductionHorticultureGeography

Abstract

fetched live from OpenAlex

The expansion of soybean [ Glycine max (L.) Merr.] production onto the Canadian Prairies has resulted in new environmental constraints that affect soybean phenology, seed yield, and seed quality. This study examined these factors for 10 soybean cultivars differing in maturity group (MG) rating from 000.9 to 1.3 in southern Manitoba (MB) and eastern Ontario (ON). Detailed climate and phenological data collected at both locations were used to explore the environmental factors and differences in measurements among MG and between locations. In MB, more time was spent in vegetative growth and less time developing flowers and seeds than in ON. The longer vegetative growth stage in MB resulted in more leaves produced on the main stem at flowering than in ON. The leaf appearance rate was consistent between locations and the rate of phenological development in the vegetative stage was greater in ON because of its warmer mean temperature and shorter photoperiod. In MB, seed yield was positively correlated with precipitation in all growth stages and had a strong correlation with precipitation during reproductive development. In ON, increasingly warmer temperatures during reproductive development had the greatest influence on seed yield, particularly in the seed development stage. This study is a baseline for soybean phenology, seed yield, and seed quality components for early MG and will aid in the optimization of soybean breeding and production in the Canadian Prairies.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.923

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 head0.085
GPT teacher head0.257
Teacher spread0.173 · 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

Citations22
Published2022
Admission routes4
Has abstractyes

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