{"id":"W4416443295","doi":"10.5376/be.2025.15.0020","title":"Case Study: Successful Genetic Improvements in Tea Cultivation in China","year":2025,"lang":"","type":"article","venue":"Biological Evidence","topic":"Tea Polyphenols and Effects","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Genetic diversity; China; Molecular breeding; Quality (philosophy); Plant breeding; Camellia sinensis; Gene pool","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008067433,0.00040589,0.0002661125,0.000687359,0.00297631,0.0007547817,0.0009030694,0.001419499,0.003965007],"category_scores_gemma":[0.001430428,0.0001039733,0.0005343365,0.001231756,0.0008377248,0.0005713805,0.001029602,0.0009979036,0.0004629446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002087536,"about_ca_system_score_gemma":0.0026088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01633247,"about_ca_topic_score_gemma":0.02853694,"domain_scores_codex":[0.9991816,0.0001848272,0.0000441958,0.0001489685,0.0002293085,0.0002111504],"domain_scores_gemma":[0.9995234,0.00007657999,0.00007726192,0.00007405115,0.00007618445,0.0001726786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"case_report","study_design_scores_codex":[0.0004608397,0.001547037,0.1986603,0.0006786728,0.0002142021,0.3782946,0.03084041,0.004597505,0.03376624,0.02858208,0.02286245,0.2994957],"study_design_scores_gemma":[0.0001287426,0.002444485,0.2446045,0.0004091171,0.0004786736,0.2992513,0.03724815,0.01530474,0.06227169,0.01469446,0.3228965,0.0002676099],"study_design_candidate":"case_report","study_design_consensus":"case_report","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379146,0.001275472,0.007846372,0.01024152,0.0002030144,0.0003843804,0.0005135075,0.0002590836,0.041362],"genre_scores_gemma":[0.9720607,0.0009741069,0.006791191,0.001181995,0.00005690011,0.00008256366,0.0002600548,0.00005365648,0.0185389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01633247,"threshold_uncertainty_score":0.03247482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05741213492921334,"score_gpt":0.362588564310153,"score_spread":0.3051764293809397,"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."}}