{"id":"W7146502118","doi":"","title":"1.ISRS2009Edmontonから、18F-標識法の総括　2.高比放射能[11C]CH3I: 製造、標識及び応用-放医研の取り組みについて","year":2010,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.009160109,0.001530534,0.0008034257,0.005698364,0.002470298,0.007298499,0.002598754,0.003775311,0.1601199],"category_scores_gemma":[0.008227075,0.0008873614,0.0005403246,0.005257367,0.001175542,0.004390825,0.00215782,0.002412172,0.1385855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006211254,"about_ca_system_score_gemma":0.01546544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1098877,"about_ca_topic_score_gemma":0.1695803,"domain_scores_codex":[0.9942597,0.000443367,0.0003221121,0.0004634707,0.003875112,0.0006362269],"domain_scores_gemma":[0.9889958,0.0005438423,0.0004718392,0.0009644203,0.007966475,0.001057625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005169144,0.000115678,0.002164978,0.0002880308,0.00001847038,0.0001454735,0.0002141663,0.0005333821,0.006929542,0.01726342,0.8031116,0.1686985],"study_design_scores_gemma":[0.00002464984,0.00004294918,0.001926785,0.0001127388,0.00001134985,0.00006135107,0.00007353329,0.0002126823,0.004359576,0.001027058,0.9921219,0.00002539619],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008536361,0.004030565,0.02604997,0.01479836,0.005270366,0.001203998,0.04322102,0.009796566,0.8870928],"genre_scores_gemma":[0.01784809,0.002266995,0.04234841,0.002434852,0.0005693464,0.0002669567,0.08324213,0.002570402,0.8484528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1601199,"threshold_uncertainty_score":0.5356545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00963669859315869,"score_gpt":0.2286927414485916,"score_spread":0.219056042855433,"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."}}