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Record W6903359040 · doi:10.1139/cjps10139

Oat mega-environments and test-locations in Quebec

2011· article· en· W6903359040 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsCultivarCoastal zoneBiplotExclusive economic zoneDry zoneYield (engineering)

Abstract

fetched live from OpenAlex

Yan, W., Pageau, D., Frégeau-Reid, J., Lajeunesse, J., Goulet, J., Durand, J. and Marois, D. 2011. Oat mega-environments and test-locations in Quebec. Can. J. Plant Sci. 91: 643-649. The Quebec agricultural regions have traditionally been divided into three zones. Zone 1 includes the small, southern most regions in the Montreal plain, zone 3 includes the large, discontinuous northern regions spreading from the west to the east of the province, and zone 2 includes areas between zone 1 and zone 3. Genotypic main effect (G) plus genotype-by-environment interaction (GGE) biplot analysis of the yield data from the Quebec Oat Registration and Recommendation trials during 2006-2009 revealed that the Quebec oat-growing regions can be divided into two distinct mega-environments: the small zone 1 region and the much larger zone 2 plus zone 3 regions. Due to the large genotype by mega-environment interactions, cultivar evaluation and recommendation must be conducted specifically to each mega-environment. However, a zone 3 test location, La Pocatière, consistently behaved like a zone 1 location in terms of cultivar ranking. Therefore, La Pocatière cannot be used to represent the zone 3 region, and cultivar recommendation for the regions represented by this location should be based on data from this location plus the zone 1 locations. In addition, climatic and soil data were examined in an attempt to explain this observation. The methodology adopted in this work may be of value to similar studies for other crops and in other regions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.994

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 head0.405
GPT teacher head0.204
Teacher spread0.201 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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