{"id":"W1996824277","doi":"10.2135/cropsci2005.0170","title":"Defining Sunflower Selection Strategies for a Highly Heterogeneous Target Population of Environments","year":2005,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biology; Sunflower; Selection (genetic algorithm); Helianthus annuus; Population; Hybrid; Predictability; Adaptation (eye); Local adaptation; Contrast (vision); Statistics; Agronomy; Computer science; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001984139,0.00005395536,0.00006056097,0.00001318924,0.0002299144,0.00006669989,0.000139173,0.00002705713,0.0000413264],"category_scores_gemma":[0.00001626369,0.0000237645,0.00002657152,0.0001784873,0.00006241108,0.0001618237,0.00002189225,0.00002249685,0.000004745689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001832143,"about_ca_system_score_gemma":0.000009687134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009513655,"about_ca_topic_score_gemma":0.0001431249,"domain_scores_codex":[0.9993706,0.000006257469,0.0001154597,0.0001735632,0.0001610751,0.0001730766],"domain_scores_gemma":[0.9998252,0.00002936373,0.00006386823,0.00002053037,0.00002268895,0.00003838796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006514031,0.00001707231,0.01083499,0.000001799202,0.000001083314,7.285662e-8,0.00004951197,0.004736063,0.9719715,0.0007744889,0.00001619888,0.01159075],"study_design_scores_gemma":[0.0001872813,0.0006184074,0.5809674,0.00002392669,0.00001191215,0.00001712332,0.0002090836,0.03031158,0.3699424,0.002086887,0.01529427,0.0003297735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993532,0.00005997078,0.0001770864,0.0000799869,0.00006867187,0.00008285986,0.00001807325,0.00000990967,0.0001501833],"genre_scores_gemma":[0.9974059,0.000005385749,0.002434533,0.00003028121,0.00006289129,0.000004117632,0.00001537642,3.679507e-7,0.00004114526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6020291,"threshold_uncertainty_score":0.1768339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759041217493157,"score_gpt":0.2208439264260088,"score_spread":0.2032535142510772,"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."}}