{"id":"W2076311126","doi":"10.7202/003464ar","title":"ROSS: Semantic Dictionary for Text Understanding and Summarization","year":2002,"lang":"fr","type":"article","venue":"Meta Journal des traducteurs","topic":"Literature, Language, and Rhetoric Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Linguistics; Humanities; Philosophy; Computer science; Natural language processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002510636,0.001408712,0.001608326,0.004951333,0.00120947,0.004193654,0.002042736,0.001781152,0.023363],"category_scores_gemma":[0.009733999,0.001023404,0.0014284,0.004983615,0.001188353,0.009627267,0.00361746,0.002187585,0.01601378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009907264,"about_ca_system_score_gemma":0.002663397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003307084,"about_ca_topic_score_gemma":0.00371521,"domain_scores_codex":[0.9971847,0.0008875772,0.0006074315,0.000550047,0.0006338941,0.0001362064],"domain_scores_gemma":[0.9960575,0.001578857,0.0002757508,0.001037805,0.0008848845,0.0001652448],"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":[0.000536354,0.0001008313,0.0007998087,0.002958742,0.0001592195,0.0003193298,0.002379892,0.004751466,0.01619516,0.1429215,0.1576604,0.6712174],"study_design_scores_gemma":[0.0001757258,0.0001647856,0.001003833,0.0007764004,0.0001287428,0.0006693731,0.001277529,0.06269855,0.02432851,0.1381649,0.7704493,0.0001622932],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002711094,0.001110859,0.939812,0.000938237,0.0002710157,0.0006221423,0.01106423,0.03642205,0.007048448],"genre_scores_gemma":[0.02634313,0.001392381,0.9367225,0.0003900022,0.0001257599,0.0007505931,0.02216145,0.003765975,0.008348147],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.023363,"threshold_uncertainty_score":0.07815701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1060726652056354,"score_gpt":0.2967473193659344,"score_spread":0.1906746541602991,"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."}}