{"id":"W2916121146","doi":"10.1525/fq.2009.62.4.20","title":"Films of the year, 2008","year":2009,"lang":"en","type":"article","venue":"Film Quarterly","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Art; Zhàng; Art history; History; Computer science; Archaeology; Artificial intelligence; China","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":[],"consensus_categories":[],"category_scores_codex":[0.00007764877,0.00006079593,0.000174794,0.00004110998,0.00003978294,0.000007867062,0.0001352718,0.00003240625,0.0003773513],"category_scores_gemma":[0.00002706783,0.00004925139,0.00008872914,0.0001167293,0.00003073364,0.00004291996,0.000006266301,0.00005757387,0.0003198339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006965917,"about_ca_system_score_gemma":0.000006184752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003567635,"about_ca_topic_score_gemma":0.00001495776,"domain_scores_codex":[0.9994977,0.000003900891,0.0002315621,0.0001178533,0.00002233783,0.0001266183],"domain_scores_gemma":[0.9996018,0.00001517656,0.0001153816,0.0002310448,0.00001324499,0.00002333857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003111453,0.0002943338,0.08204774,0.00004211296,0.00008840118,0.000004191952,0.01289856,0.00001285809,0.0004520564,0.2562969,0.6306269,0.01720484],"study_design_scores_gemma":[0.000897995,0.0006725423,0.7853634,0.00003128243,0.000009027279,0.000003648509,0.001043673,0.0001821699,0.0003345623,0.04254644,0.1686606,0.0002546869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7883717,0.002617695,0.0001639775,0.004796429,0.001444365,0.0002514799,0.0001764755,0.00003794807,0.2021399],"genre_scores_gemma":[0.9936348,0.00002573331,0.0001752462,0.0002471333,0.0000634009,0.000003669247,0.000002113758,0.000004291615,0.005843643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7033157,"threshold_uncertainty_score":0.4131732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01599766477682729,"score_gpt":0.2001919369879589,"score_spread":0.1841942722111316,"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."}}