{"id":"W3022542830","doi":"10.34133/2020/3414926","title":"Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography","year":2020,"lang":"en","type":"article","venue":"Plant Phenomics","topic":"GABA and Rice Research","field":"Agricultural and Biological Sciences","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities; Chinese Academy of Sciences; Institute of Genetics; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computed tomography; Panicle; Support vector machine; Random forest; Machine learning; Computer science; Mathematics; Biology; Agronomy; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000172097,0.0001656539,0.0002939267,0.00005730747,0.0001849998,0.0001112398,0.0003328846,0.00006484072,0.0001080241],"category_scores_gemma":[0.00004069076,0.00006986604,0.0001766488,0.001925162,0.0000414113,0.0001056157,0.0000964908,0.0001786007,0.00003282709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002568807,"about_ca_system_score_gemma":0.00001165423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002441344,"about_ca_topic_score_gemma":0.0001842066,"domain_scores_codex":[0.9986761,0.00008092198,0.0002321751,0.0004006401,0.000235748,0.0003743963],"domain_scores_gemma":[0.9992705,0.0001906418,0.00007386485,0.00004328974,0.00007988254,0.0003418385],"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":[0.0001101379,0.00008886631,0.001475257,0.00001064533,0.0001634571,0.00002864698,0.0005585619,0.001366304,0.9842662,0.0000341848,0.0004818554,0.01141586],"study_design_scores_gemma":[0.0005463734,0.0005516949,0.718356,0.00002265799,0.0005418634,0.00002396488,0.001776122,0.2651498,0.003916254,0.0001653079,0.007898624,0.001051317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946934,0.00005659671,0.002798226,0.001145661,0.00004278365,0.0002147616,0.0006943024,0.0000619437,0.0002923143],"genre_scores_gemma":[0.9884135,0.00001424424,0.01030836,0.0006327059,0.000363079,0.000003109247,0.000246322,0.000001807593,0.0000169123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.98035,"threshold_uncertainty_score":0.2849054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03802114046337267,"score_gpt":0.2516755600328796,"score_spread":0.2136544195695069,"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."}}