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Record W6941019262 · doi:10.1139/cjps2012-032

A retrospective look at short-season soybean cultivar development in Ontario

2012· article· en· W6941019262 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCultivarYield (engineering)CropGrowing seasonCrop yield

Abstract

fetched live from OpenAlex

Cober, E. R. and Voldeng, H. D. 2012. A retrospective look at short-season soybean cultivar development in Ontario. Can. J. Plant Sci. 92: 1239-1243. Canadian soybean production has increased from about 63 000 ha in 1951 to 1.2 million ha in 2006 with expansion from southern Ontario to shorter season areas. From 1971 to 2000, soybean production in Ontario increased sixfold. Using annual reports from 1971 to 2000 of short-season trials in Ontario, we found that the number of reported cultivars remained between three and five until 1984 when the number of cultivars started to increase at the rate of about 2.5 yr-1. The amount of time that cultivars remained in the reports has declined from 12 to 4 yr on average over the 30-yr period. Long-lived cultivars, however, were developed throughout the period including: Hardome, Merit, Evans, Maple Arrow, Bicentennial, Maple Glen, KG 41, OAC Eclipse, 9071, OAC Bayfield, PS 36, and S03-W4. Over the 30-yr period of cultivar testing, short-season seed yield increased about 600 kg ha-1, while mean time to maturity decreased about 10 d. During the same time, mean Ontario commercial seed yields increased about 750 kg ha-1. Over the 30-yr period, the number of short-season cultivars in annual reports increased 36-fold, while provincial yield increased only 1.4-fold. If the number of cultivars in annual reports is an indication of breeding effort, large research investments may be necessary to increase future yield improvement.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.226
GPT teacher head0.229
Teacher spread0.004 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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