{"id":"W24174537","doi":"10.1093/nar/gkt958","title":"職員の働き甲斐やモチベーションを向上させ、いい仕事に結びつく賃金評価制度の開発 －仕事の変容と学生の「学びと成長」の視点から","year":2011,"lang":"en","type":"article","venue":"Medical Entomology and Zoology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","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":[0.004818187,0.0006180033,0.001235651,0.007401732,0.000923875,0.003978372,0.001039618,0.0008469922,0.01679231],"category_scores_gemma":[0.006122929,0.0004825978,0.001339481,0.007096294,0.0006674318,0.001596876,0.001535231,0.001464704,0.00863663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009746928,"about_ca_system_score_gemma":0.002048057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007954232,"about_ca_topic_score_gemma":0.01439252,"domain_scores_codex":[0.9974313,0.0004585003,0.0003580929,0.0008105459,0.0007788843,0.0001625536],"domain_scores_gemma":[0.9948754,0.001694357,0.0008233875,0.0009547318,0.001077914,0.0005741646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00153352,0.0001328947,0.08276609,0.003715458,0.0006681379,0.001473592,0.001107325,0.0009617117,0.05437643,0.01135506,0.1155995,0.7263104],"study_design_scores_gemma":[0.0001148682,0.0002253767,0.1267718,0.001354862,0.000471917,0.005473266,0.0005900715,0.001021219,0.01909403,0.01287675,0.8317996,0.0002062662],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2093225,0.1061534,0.1297375,0.008970756,0.003987772,0.0007088633,0.423093,0.006643984,0.1113822],"genre_scores_gemma":[0.2235473,0.05491563,0.1667232,0.003093884,0.001720861,0.0004580556,0.524627,0.003013745,0.02190026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01679231,"threshold_uncertainty_score":0.05617583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131279758809033,"score_gpt":0.2312041389748261,"score_spread":0.2198913413867358,"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."}}