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Record W2389483589

Stability Analysis for Elementary Characters of Hybrid Rice by AMMI Model

2002· article· en· W2389483589 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.

Bibliographic record

VenueZuo wu xue bao · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsAmmiAdaptabilityStability (learning theory)MathematicsYield (engineering)Main effectGrain yieldInteractionLinear regressionGene–environment interactionStatisticsHorticultureBiologyGenotypeComputer scienceEcologyGeneticsMachine learning
DOInot available

Abstract

fetched live from OpenAlex

A 3 site NCⅡ experiment was carried out for 45 hybrid rice varieties crossed by 5 CMS lines×9 Restorer lines, which are widely used recently. The characters′ performance in stability was analysed by AMMI model, and the results showed: (1) The G×E interaction were prominent; For yield and most characters environmental effect accounted for the most to the total variation, genotype and G×E interaction were on the 2nd and 3th orders respectively; But for 1000 grain weight, the effects of environment and genotype were equal. (2) The proportion of linear to non linear components in G×E interaction was varied with genotypes and characters. Some varieties or characters were mainly linear effect and others non linear effect. (3) No obvious contradiction existed between high yield and stability; It is possible to develop stable and high yield varieties. (4) AMMI model is an effective and accurate way for stability and adaptability measurement Comparatively, regression model is short in accurate and effectiveness.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.829

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)0.0010.000

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 head0.029
GPT teacher head0.204
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