{"id":"W4388525005","doi":"10.18280/ijdne.180509","title":"Harnessing Heterogeneity: Clustering Kazakh Spring Rapeseed for Breeding Value","year":2023,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Phytase and its Applications","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Agriculture of the Republic of Kazakhstan","keywords":"Kazakh; Rapeseed; Spring (device); Cluster analysis; Value (mathematics); Agricultural engineering; Geography; Biology; Engineering; Agronomy; Statistics; Mathematics; Structural engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000414214,0.00008827503,0.0001275868,0.00006094142,0.0001375452,0.0001385538,0.0002698703,0.0001031323,0.000004405085],"category_scores_gemma":[0.00006532265,0.00004271497,0.00009821649,0.0001670267,0.00001747826,0.0002411764,0.0000640204,0.0001893798,0.000002223514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000338726,"about_ca_system_score_gemma":0.00001129331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006570562,"about_ca_topic_score_gemma":0.00004131736,"domain_scores_codex":[0.9992661,0.00001895949,0.0002475279,0.0001237825,0.00019625,0.0001473566],"domain_scores_gemma":[0.999258,0.0002728599,0.0001887132,0.00002344817,0.0001942728,0.0000627214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002305527,0.00007407094,0.004131937,0.00001720428,0.0002690125,0.00003451451,0.0001497465,0.004209994,0.8390034,0.002646096,0.0007763575,0.1484571],"study_design_scores_gemma":[0.002521193,0.001095774,0.1636246,0.0007094603,0.0002234227,0.0007387282,0.00111811,0.7147353,0.03254289,0.02568475,0.0558782,0.001127521],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820676,0.0002941607,0.01439804,0.002396618,0.0006029002,0.0001174457,0.00003491316,0.00002840878,0.00005993837],"genre_scores_gemma":[0.9963409,0.0002551273,0.002370335,0.0002753961,0.0007043142,0.000004516182,0.00002174,0.000001990689,0.00002566178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8064605,"threshold_uncertainty_score":0.1741866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03182976784504105,"score_gpt":0.2805398106024987,"score_spread":0.2487100427574576,"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."}}