{"id":"W7098664890","doi":"","title":"Language Technologies, Montréal: Canada (2012)" NOMIT: Automatic Titling by Nominalizing","year":2012,"lang":"en","type":"article","venue":"","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Relevance (law); Process (computing); Subject (documents); Task (project management); Semantics (computer science)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002696592,0.002036439,0.001204559,0.005350628,0.004089547,0.01168717,0.002088942,0.001614503,0.1551847],"category_scores_gemma":[0.006818954,0.001754376,0.00110489,0.00771047,0.002618102,0.005408082,0.002349189,0.002180763,0.09948052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01503822,"about_ca_system_score_gemma":0.01624846,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7351316,"about_ca_topic_score_gemma":0.7909083,"domain_scores_codex":[0.9973657,0.00040213,0.0002279898,0.0005355074,0.001195303,0.0002734313],"domain_scores_gemma":[0.9948581,0.001016292,0.0002053935,0.0006319382,0.002956851,0.0003314015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000720496,0.00001560252,0.0009213268,0.0004993227,0.00002658133,0.0001986546,0.0007977854,0.0006256809,0.003781603,0.02617172,0.673286,0.2936036],"study_design_scores_gemma":[0.00001614877,0.00001046364,0.002295821,0.0002480103,0.00001703376,0.000175075,0.0004987539,0.002168353,0.00216214,0.0052499,0.9870893,0.00006878511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005044153,0.02738675,0.3133545,0.01448763,0.002928906,0.0005695674,0.1222597,0.09230815,0.4216607],"genre_scores_gemma":[0.04121487,0.01697481,0.2548375,0.002206436,0.0005617127,0.0006694996,0.1151857,0.02480819,0.5435413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7351316,"threshold_uncertainty_score":0.5328566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503776960602232,"score_gpt":0.1914827497228989,"score_spread":0.1664449801168766,"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."}}