{"id":"W2365709193","doi":"","title":"Factor Analysis of Quantitative Characters in Japonica Rice Restorer Lines","year":2007,"lang":"en","type":"article","venue":"Seed","topic":"Agriculture, Soil, Plant Science","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Japonica rice; Japonica; Factor (programming language); Biology; Traditional medicine; Botany; Computer science; Medicine","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":[0.0003809126,0.000104154,0.0002469058,0.00006376755,0.00005412944,0.00001563675,0.0002694744,0.00007961237,0.00001004991],"category_scores_gemma":[0.0001511773,0.00003457768,0.0001061842,0.002439162,0.00007558877,0.0001361226,0.00003830329,0.00009736195,0.00001746963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000286161,"about_ca_system_score_gemma":0.000005638027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009394148,"about_ca_topic_score_gemma":0.006920435,"domain_scores_codex":[0.9989105,0.00004637032,0.0002764186,0.0002550319,0.000255369,0.0002563109],"domain_scores_gemma":[0.9991415,0.0004952571,0.0001604574,0.00004938978,0.00008086349,0.00007250353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005776461,0.00008445567,0.5120864,0.000002862554,0.00005647831,0.000007831193,0.0006836876,0.00002001954,0.486074,0.0003604515,0.000022952,0.0005431502],"study_design_scores_gemma":[0.00004756542,0.00006787586,0.9947624,0.000008007407,0.00003080596,5.529604e-7,0.0007135902,0.00008816517,0.004021755,0.00002229712,0.0001307385,0.0001063137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988697,0.00005118378,0.000004248097,0.0002932561,0.00006806398,0.00009233003,0.00005824073,0.00002293253,0.0005400883],"genre_scores_gemma":[0.9994933,0.00001461538,0.0001255664,0.00009887465,0.00003533771,0.000001643271,0.00006194471,3.243235e-7,0.0001684126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4826759,"threshold_uncertainty_score":0.3861765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03128336254511694,"score_gpt":0.2651313797280207,"score_spread":0.2338480171829038,"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."}}