{"id":"W7133062113","doi":"","title":"Training Tomorrow's Historians","year":2012,"lang":"en","type":"article","venue":"TSpace","topic":"Earthquake and Disaster Impact Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Training (meteorology); Professional development; Process (computing); Work (physics); Interpretation (philosophy)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004102548,0.00006463657,0.00009651462,0.00002613941,0.0004158301,0.00002554031,0.00008657903,0.00004201927,0.0005221309],"category_scores_gemma":[0.0001921591,0.00006099603,0.00004190297,0.0001198242,0.0001102312,0.0002060422,0.00002012948,0.0000597518,0.0002798185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006572777,"about_ca_system_score_gemma":0.00006204157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001849933,"about_ca_topic_score_gemma":0.00135985,"domain_scores_codex":[0.9991576,0.00006677103,0.00006517932,0.00007253408,0.0001986667,0.0004392336],"domain_scores_gemma":[0.9995999,0.00007434833,0.00003113116,0.00009275598,0.00002247179,0.0001793447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003383196,0.00002404076,0.006926388,0.000002317005,0.000008584816,9.364597e-7,0.9593595,2.545535e-7,0.00008803,0.0110128,0.01235746,0.01021635],"study_design_scores_gemma":[0.00007929579,0.00001363753,0.004022648,0.000005122121,0.000009480851,6.245205e-7,0.2068,2.825007e-7,0.00003379515,0.0001301965,0.7888112,0.00009373161],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3130265,0.0009190631,0.00007763738,0.002699243,0.0009251618,0.00006168363,5.807964e-7,0.00009772932,0.6821924],"genre_scores_gemma":[0.9645163,0.00003341738,0.0002298707,0.0003669501,0.001132156,0.000004312536,4.832286e-7,0.000006639563,0.03370986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7764537,"threshold_uncertainty_score":0.5716967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2139452613154472,"score_gpt":0.4536310350635696,"score_spread":0.2396857737481224,"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."}}