{"id":"W7144928317","doi":"","title":"カナダにおける「クリティカル・シンキング」","year":2002,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002406407,0.0006555782,0.0005323767,0.0002791958,0.001062981,0.0001914096,0.00073222,0.0005872356,0.001225321],"category_scores_gemma":[0.0003540563,0.0007107307,0.000218864,0.0006545245,0.001088296,0.001615986,0.0002772591,0.00110935,0.001421696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000375394,"about_ca_system_score_gemma":0.0001549129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004132083,"about_ca_topic_score_gemma":0.0001406354,"domain_scores_codex":[0.9967139,0.00007652763,0.0008634179,0.0008125226,0.0007041612,0.0008295088],"domain_scores_gemma":[0.9978787,0.0001842502,0.0001171906,0.001347499,0.0001987033,0.0002736974],"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":[0.0001104866,0.0005071484,0.001581086,0.0007276817,0.00071978,0.002982388,0.0009235769,0.006048353,0.007808901,0.8940126,0.08228567,0.002292358],"study_design_scores_gemma":[0.002241518,0.000409709,0.002920163,0.001314991,0.0004608293,0.002741402,0.001121365,0.02856117,0.01096065,0.006479229,0.9400179,0.002771074],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1688713,0.08493905,0.007520228,0.002606333,0.02915407,0.001150414,0.005081498,0.003726358,0.6969507],"genre_scores_gemma":[0.9883033,0.002743884,0.002843273,0.0001145029,0.001993602,0.00007169061,0.0005792482,0.00005530729,0.00329521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8875334,"threshold_uncertainty_score":0.9996877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01944589890812741,"score_gpt":0.2211828698669175,"score_spread":0.2017369709587901,"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."}}