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Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis

2006· article· en· W2081292310 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Agronomy and Crop Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsAmmiPath coefficientPath analysis (statistics)Grain yieldGenotypeGene–environment interactionYield (engineering)AgronomyMathematicsCorrelationSpring (device)Crop yieldCropPrincipal component analysisBiologyStatisticsEngineering

Abstract

fetched live from OpenAlex

Abstract An understanding of the characteristics of crop varieties and advanced lines could help improve their cultivation and to further enhance their potential. The objectives of this study were to estimate the genotype (G), environment (E) and genotype × environment (GE) interactions on the grain yield of Chinese spring wheat genotypes in 2000 and 2001 by the additive main effects and multiplicative interaction (AMMI) model, and to evaluate the relationships between yield and its components by correlation and path analysis. Grain yield varied from 3.9 to 5.2 t ha −1 , among which SW8188 had the highest yield performance, followed by 58769‐6 and Chuannong 16. Three interaction principal components (IPC) accounted for a total of 79.99 % and 72.96 % of the interactions with 41.05 % and 52.08 % for the corresponding degrees of freedom in 2000 and 2001, respectively. When IPC3 was significant, the stability coefficient D i was more useful in the evaluation of the stability of each genotype. The estimates of D i in the 2 years indicated that the D i values varied between genotypes and years. The D i values ranged from 1.804 to 14.665 and 2.497 to 12.481 in 2000 and 2001 respectively. The suitable locations (environments) for all genotypes were characterized. These results would be useful for improving the Chinese spring wheat cultivation and 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.

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 score0.419
Threshold uncertainty score0.090

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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