{"id":"W1871378960","doi":"","title":"Extending the Entity-based Coherence Model with Multiple Ranks","year":2012,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Coreference; Coherence (philosophical gambling strategy); Computer science; Pairwise comparison; Artificial intelligence; Natural language processing; Sentence; Training set; Set (abstract data type); Component (thermodynamics); Information retrieval; Statistics; Resolution (logic); Mathematics; Programming language","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":[0.0003317923,0.00008589725,0.00007136853,0.00002810351,0.0001449729,0.0000995962,0.00066523,0.0000253583,0.00002121607],"category_scores_gemma":[0.00001932968,0.00004824973,0.00002658395,0.000128282,0.00003002342,0.0004889795,0.0001178317,0.000104928,0.00003206746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002036848,"about_ca_system_score_gemma":0.00004499451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000604355,"about_ca_topic_score_gemma":0.00002719598,"domain_scores_codex":[0.9991632,0.00003174887,0.0001002671,0.000183577,0.0002318507,0.0002893663],"domain_scores_gemma":[0.9991814,0.0001036068,0.00003634783,0.0005745197,0.00003336882,0.00007073915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002083411,0.0002281463,0.09190506,0.00003454827,0.00003059726,0.000004607673,0.002613362,0.442621,0.002459644,0.4027874,0.0008253078,0.05646949],"study_design_scores_gemma":[0.0002183083,0.000007990548,0.000734816,0.000007806353,0.000002796602,0.000002841618,0.00001370313,0.9966261,0.001492064,0.0004704075,0.0003329036,0.0000903055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03148101,0.00005633752,0.9643075,0.0004762459,0.0001223325,0.0001234893,3.22509e-7,0.000140797,0.003291895],"genre_scores_gemma":[0.731889,5.903839e-7,0.2672393,0.0004423745,0.00003149435,0.00001390339,2.248132e-7,0.000003833848,0.0003792677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.700408,"threshold_uncertainty_score":0.1967567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0360831324396391,"score_gpt":0.2470612900098579,"score_spread":0.2109781575702188,"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."}}