{"id":"W3145955739","doi":"","title":"基于RNA-Seq技术的“紫娟”茶树转录组分析","year":2015,"lang":"zh","type":"article","venue":"分子植物育种","topic":"Medicinal Plant Pharmacodynamics Research","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"RNA-Seq; Computer science; Transcriptome; Biology; Genetics; Gene; Gene expression","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":[0.002925714,0.0009902098,0.001153065,0.001002332,0.001883435,0.002933896,0.0008366311,0.0009918682,0.007892657],"category_scores_gemma":[0.002322342,0.00101073,0.001751936,0.001254927,0.001163723,0.001771632,0.0007986226,0.002575955,0.004924578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688083,"about_ca_system_score_gemma":0.003831492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003297131,"about_ca_topic_score_gemma":0.0065451,"domain_scores_codex":[0.9978524,0.0004443249,0.0002214592,0.000844179,0.0004855245,0.0001519939],"domain_scores_gemma":[0.9987269,0.0004273473,0.0001124456,0.000198573,0.0004522274,0.00008263864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009690634,0.0001616249,0.01069146,0.003166045,0.0004305051,0.0003569884,0.001528392,0.006615431,0.7912197,0.0227754,0.02180233,0.1402832],"study_design_scores_gemma":[0.0001672012,0.0004074535,0.02549382,0.0003826609,0.0006805114,0.0006618968,0.001109014,0.02546105,0.632117,0.02587416,0.2872406,0.0004045565],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1435639,0.005921768,0.6891826,0.003438403,0.002546376,0.002099056,0.0467138,0.01216662,0.0943675],"genre_scores_gemma":[0.2297375,0.002658901,0.689229,0.004323511,0.0003625445,0.002783332,0.03791182,0.003764239,0.02922919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007892657,"threshold_uncertainty_score":0.02640361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3890089199459478,"score_gpt":0.54164743825423,"score_spread":0.1526385183082822,"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."}}