{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001193733,0.0003834829,0.0007061685,0.0003083917,0.0001042591,0.00005431099,0.0003122787,0.0003527968,0.0001682717],"category_scores_gemma":[0.003475984,0.0002785816,0.00008752639,0.001340109,0.0001611873,0.0001819204,0.0003685764,0.0006335218,0.00004130019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003878076,"about_ca_system_score_gemma":0.0001988359,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0102707,"about_ca_topic_score_gemma":0.001210414,"domain_scores_codex":[0.996381,0.0007247842,0.0009906916,0.0009718069,0.000252029,0.0006796203],"domain_scores_gemma":[0.9984574,0.0006686259,0.000177884,0.0004916319,0.00005952051,0.0001449218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003832663,0.001658623,0.891614,0.0002236206,0.00002924763,0.00766047,0.001073684,0.00002579914,0.007675533,0.00002355581,0.00004049089,0.08959176],"study_design_scores_gemma":[0.002876372,0.002903592,0.9872735,0.002362814,0.00005337483,0.00009918017,0.002263152,0.001394091,0.0003260705,0.0001463776,0.00003556116,0.0002658752],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882283,0.007075947,0.0001053803,0.0007488945,0.0005341336,0.003143165,0.000003621666,0.0000366243,0.0001239894],"genre_scores_gemma":[0.9978495,0.0009712348,0.00009678055,0.0004968487,0.000123471,0.0002603723,0.000002758288,0.000009820524,0.0001891748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0956596,"threshold_uncertainty_score":0.9999666,"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."}}