{"id":"W27461082","doi":"10.1038/npp.2016.133","title":"P-48 多孔性アルギン酸/リン酸オクタカルシウム複合体の調製とキャラクタリゼーション(インプラント,一般講演(ポスター発表),第54回日本歯科理工学会学術講演会)","year":2009,"lang":"en","type":"article","venue":"歯科材料・器械","topic":"Transcranial Magnetic Stimulation Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001625497,0.0003547203,0.0001851064,0.0001798997,0.0002477355,0.0002856285,0.0001665222,0.0004170501,0.007698327],"category_scores_gemma":[0.0002360541,0.00008462152,0.0001664652,0.000142149,0.0003609498,0.0002188764,0.0001876355,0.0005208406,0.002816848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002450347,"about_ca_system_score_gemma":0.0002374482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007583701,"about_ca_topic_score_gemma":0.0009797375,"domain_scores_codex":[0.9999381,0.000006491705,0.000004870908,0.0000197316,0.00001828226,0.00001252835],"domain_scores_gemma":[0.9999129,0.00001319884,0.00002302149,0.00000765886,0.00002551561,0.00001771576],"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":[0.0008756195,0.000109159,0.003030311,0.0001745636,0.00003786708,0.0008023623,0.00008714193,0.0001712076,0.8794124,0.0009959636,0.001250334,0.1130532],"study_design_scores_gemma":[0.0001132279,0.003161706,0.1111808,0.00008159359,0.0001044496,0.005702379,0.0002330791,0.00280727,0.8247804,0.004346461,0.04744684,0.00004185235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9014712,0.006350773,0.02701853,0.0005547611,0.0003001077,0.0002643052,0.0008011344,0.0005855379,0.06265366],"genre_scores_gemma":[0.9704166,0.001829472,0.006558965,0.0001597595,0.00008962927,0.0001030666,0.0003832192,0.00004536969,0.02041381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007698327,"threshold_uncertainty_score":0.0257535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04785000065540149,"score_gpt":0.2977043020137508,"score_spread":0.2498543013583493,"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."}}