{"id":"W196680015","doi":"","title":"A241 マルチNMRセンサーによる水電解運転時のPEM内含水量の分布計測(超音波・電磁波による熱流体計測2)","year":2006,"lang":"ja","type":"article","venue":"熱工学コンファレンス講演論文集","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sciencetech (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":[0.0003808932,0.0003816174,0.0002146559,0.000514015,0.001756659,0.001414922,0.0005950848,0.001188587,0.05525518],"category_scores_gemma":[0.000858478,0.0001631545,0.0003648251,0.0003435886,0.001004302,0.00112439,0.0006283559,0.0008194197,0.01081598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007306353,"about_ca_system_score_gemma":0.0007593156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001901575,"about_ca_topic_score_gemma":0.00149076,"domain_scores_codex":[0.9996396,0.00004803541,0.00001717189,0.00008442473,0.0001429507,0.00006778674],"domain_scores_gemma":[0.999696,0.00006195448,0.00003678054,0.00003304352,0.0001297035,0.00004250908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007154035,0.0006003095,0.0053064,0.0005094715,0.00007694973,0.003338275,0.001607713,0.001401131,0.1660207,0.4939199,0.04577427,0.2807294],"study_design_scores_gemma":[0.0001128992,0.0008877919,0.01997227,0.0001403353,0.00007878305,0.005429106,0.00215161,0.003046365,0.1954298,0.1051601,0.6674873,0.0001037109],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1943055,0.002506993,0.01992607,0.004895972,0.001547626,0.0001881323,0.0006650673,0.0003087976,0.7756559],"genre_scores_gemma":[0.6333336,0.001242462,0.01621442,0.00133105,0.0003778402,0.000188012,0.0005716055,0.00006566755,0.3466752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05525518,"threshold_uncertainty_score":0.184847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005667411457309013,"score_gpt":0.1873595278057902,"score_spread":0.1816921163484812,"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."}}