{"id":"W2132772540","doi":"","title":"Annotating Anaphoric Shell Nouns with their Antecedents","year":2013,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Noun; Annotation; Natural language processing; Artificial intelligence; Crowdsourcing; Quality (philosophy); World Wide Web; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006431264,0.001009508,0.00106716,0.005949214,0.00472624,0.00364809,0.001589268,0.002490381,0.009636523],"category_scores_gemma":[0.02498888,0.0009503505,0.0008809755,0.004665723,0.001876663,0.006877049,0.005182276,0.00219212,0.003465523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751341,"about_ca_system_score_gemma":0.002803943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006842491,"about_ca_topic_score_gemma":0.01385726,"domain_scores_codex":[0.9924898,0.002186242,0.0008982637,0.001968494,0.002121466,0.0003357511],"domain_scores_gemma":[0.9755706,0.01196284,0.002390519,0.004140591,0.005478619,0.000456822],"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":[0.00189023,0.0006830069,0.09979051,0.004162564,0.0002990169,0.006026078,0.03889154,0.006052494,0.1732761,0.1431038,0.06043904,0.4653856],"study_design_scores_gemma":[0.0001580386,0.0002451085,0.06706336,0.001434762,0.0004101234,0.004711337,0.01801275,0.09035432,0.130227,0.1271264,0.5599026,0.0003541995],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3578666,0.002041817,0.5383536,0.002888772,0.0008514611,0.001391966,0.007938144,0.004839072,0.0838286],"genre_scores_gemma":[0.5483947,0.0008502718,0.4204361,0.0006107096,0.0003574754,0.0005283366,0.008227277,0.001103401,0.01949185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009636523,"threshold_uncertainty_score":0.0340122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004637014399758553,"score_gpt":0.1650679154478999,"score_spread":0.1604309010481414,"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."}}