{"id":"W2586061920","doi":"10.14841/jspp.2004.0.518.0","title":"PAP1 遺伝子過剰発現体を用いた網羅的解析によるアントシアニン蓄積機構の解明 (1) ―トランスクリプトミクスとメタボロミクスの統合―","year":2004,"lang":"ja","type":"article","venue":"日本植物生理学会年会およびシンポジウム　講演要旨集 第45回日本植物生理学会年会講演要旨集","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Phenomenome Discoveries (Canada)","funders":"","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004773001,0.0003414599,0.0002249558,0.0004683937,0.001374615,0.00234748,0.0005639677,0.0006195017,0.02357197],"category_scores_gemma":[0.00142246,0.0002472084,0.000265182,0.0004607939,0.00134257,0.001665587,0.0007877969,0.0008978483,0.005930743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124004,"about_ca_system_score_gemma":0.001296951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003171169,"about_ca_topic_score_gemma":0.001859113,"domain_scores_codex":[0.999703,0.00003679129,0.000009979305,0.0001002358,0.00009096818,0.00005896778],"domain_scores_gemma":[0.999488,0.0001140311,0.00005927849,0.00005106617,0.0002290643,0.00005846608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0004634381,0.000248112,0.02107001,0.000629165,0.00008094754,0.001852228,0.005463657,0.002331664,0.1127719,0.5123994,0.02328448,0.319405],"study_design_scores_gemma":[0.00004312761,0.0005451422,0.02942316,0.0001523298,0.0001210938,0.004533691,0.006084479,0.004895737,0.2515318,0.1483871,0.5541705,0.0001119586],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2912703,0.002524526,0.02506495,0.004296805,0.0005893678,0.0001073416,0.0006239912,0.0003966438,0.6751261],"genre_scores_gemma":[0.8119726,0.001186741,0.008750159,0.0006172358,0.0001621259,0.00008920069,0.0004963829,0.00008770644,0.1766379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02357197,"threshold_uncertainty_score":0.07885611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008008089218331387,"score_gpt":0.19855542967708,"score_spread":0.1905473404587487,"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."}}