{"id":"W2081292310","doi":"10.1111/j.1439-037x.2006.00200.x","title":"Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis","year":2006,"lang":"en","type":"article","venue":"Journal of Agronomy and Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada","keywords":"Ammi; Path coefficient; Path analysis (statistics); Grain yield; Genotype; Gene–environment interaction; Yield (engineering); Agronomy; Mathematics; Correlation; Spring (device); Crop yield; Crop; Principal component analysis; Biology; Statistics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006418498,0.001981401,0.0008977519,0.002163122,0.0006100345,0.001107107,0.0008972634,0.0005069813,0.001978565],"category_scores_gemma":[0.008657657,0.0004925015,0.002479859,0.0022585,0.0007509953,0.0009103913,0.001058247,0.001169176,0.0002837045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006475115,"about_ca_system_score_gemma":0.001328604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005059276,"about_ca_topic_score_gemma":0.003453184,"domain_scores_codex":[0.99497,0.003370147,0.0001532258,0.0008043495,0.0004282517,0.0002739816],"domain_scores_gemma":[0.9918006,0.00630501,0.0007386658,0.0004447943,0.0005078678,0.0002029035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001536488,0.0008292372,0.6956995,0.0002504166,0.004338766,0.001376967,0.0007347979,0.1642151,0.01538573,0.007555249,0.001768533,0.1063092],"study_design_scores_gemma":[0.00005870751,0.0006845434,0.1940057,0.00002295168,0.0005436394,0.0002781712,0.0001912134,0.7982357,0.001711872,0.003219478,0.0009600868,0.00008799805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9052523,0.0001703231,0.09285922,0.000136355,0.00003369364,0.0001064075,0.0004685839,0.0002819181,0.0006912934],"genre_scores_gemma":[0.9710349,0.00009540112,0.0271973,0.0000231218,0.00001556667,0.0001746341,0.0006235822,0.00006821346,0.0007673137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006418498,"threshold_uncertainty_score":0.03394467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903426919242297,"score_gpt":0.2237579925444149,"score_spread":0.204723723351992,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}