{"id":"W2931628922","doi":"","title":"心筋血流低下モデルを用いた99mTc-1日法負荷心筋血流SPECT検査の投与量比に関する基礎的検討 ―心筋ファントムを用いた画像比較―","year":2018,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Environmental science","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":[0.0007734535,0.0004253922,0.000302018,0.0006499388,0.000821319,0.00154484,0.0005234662,0.001149756,0.01045652],"category_scores_gemma":[0.001164889,0.0003683171,0.0003195687,0.0005367717,0.00122146,0.001857565,0.0003821305,0.001604991,0.001815186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000988871,"about_ca_system_score_gemma":0.0009134871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003457399,"about_ca_topic_score_gemma":0.003056674,"domain_scores_codex":[0.9998085,0.0000413543,0.000009443944,0.00005712039,0.00004336105,0.00004021709],"domain_scores_gemma":[0.9996004,0.0001230963,0.00006402647,0.00003449028,0.0001168438,0.00006123273],"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":[0.002252077,0.000363015,0.0284116,0.001596731,0.0003152909,0.00552924,0.002460275,0.002296762,0.577645,0.05543299,0.01811435,0.3055827],"study_design_scores_gemma":[0.0002696885,0.001852931,0.04925925,0.0003472201,0.0007380137,0.01758745,0.002695364,0.01085966,0.7021265,0.04285936,0.1711726,0.0002319813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5597495,0.02291176,0.1201418,0.01063463,0.001136224,0.0003213174,0.001030991,0.001122643,0.282951],"genre_scores_gemma":[0.9088528,0.005393542,0.03241422,0.00158287,0.0004239336,0.000158788,0.0004879734,0.0002162502,0.05046956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01045652,"threshold_uncertainty_score":0.0349806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676452955487632,"score_gpt":0.2661502139340817,"score_spread":0.2493856843792054,"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."}}