{"id":"W6886210137","doi":"10.14989/doctor.k25172","title":"RNAファミリー配列の深層生成設計","year":2024,"lang":"ja","type":"dissertation","venue":"Kyoto University Research Information Repository (Kyoto University)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Institute of Genetics; Imperial College London; Japan Society for the Promotion of Science; Okinawa Institute of Science and Technology Graduate University; Ministry of Education, India","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0007419775,0.0008152107,0.0007002893,0.007265994,0.00233722,0.0005106556,0.002374327,0.002067093,0.000793173],"category_scores_gemma":[0.0001591114,0.001096849,0.0005197222,0.00425685,0.0007580589,0.005275254,0.0005780176,0.00416638,0.009375653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00250782,"about_ca_system_score_gemma":0.001763926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004067126,"about_ca_topic_score_gemma":0.0004109877,"domain_scores_codex":[0.9951861,0.0005201091,0.0006525681,0.0008076085,0.001515352,0.001318247],"domain_scores_gemma":[0.9961569,0.0003076593,0.0002302542,0.001159909,0.001526092,0.0006191964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003282227,0.0002896089,0.0006614115,0.007089714,0.002669173,0.01251901,0.04460601,0.005543468,0.001954385,0.8708641,0.0472954,0.003225539],"study_design_scores_gemma":[0.001425908,0.0007108747,0.0006332866,0.001161217,0.0003894226,0.0001876656,0.2331689,0.004726309,0.00224486,0.002266725,0.7516054,0.001479443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2032565,0.0003783247,0.0001165428,0.0001909751,0.001569205,0.001272157,0.0001095208,0.001202599,0.7919042],"genre_scores_gemma":[0.6715678,0.002370663,0.0001304343,0.0000249375,0.0001578847,0.000002914452,0.0009597528,0.00007031778,0.3247153],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8685973,"threshold_uncertainty_score":0.9992284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539297245911876,"score_gpt":0.2363496754034289,"score_spread":0.2209567029443102,"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."}}