{"id":"W1559568089","doi":"10.3233/978-1-60750-535-8-3","title":"Ontological Lessons from the Semantics of Mass and Count Nouns","year":2010,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Noun; Linguistics; Semantics (computer science); Computer science; Natural language processing; Programming language; 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":[0.004260273,0.0009776256,0.001074503,0.0039445,0.005359693,0.00951102,0.00252367,0.003294756,0.01010294],"category_scores_gemma":[0.01161694,0.0009331928,0.001373058,0.003425874,0.02365466,0.03297254,0.00555654,0.005108293,0.001650431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004526665,"about_ca_system_score_gemma":0.00205988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007335813,"about_ca_topic_score_gemma":0.005841356,"domain_scores_codex":[0.9954689,0.002206986,0.0003978694,0.0008090665,0.0008193974,0.0002977248],"domain_scores_gemma":[0.9937745,0.003208134,0.0004379907,0.001219066,0.00100678,0.0003534849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001135374,0.00000398044,0.00005660771,0.00001876001,0.000002534355,0.0000252201,0.001444435,0.00007385411,0.00005189179,0.9954673,0.00092541,0.001918523],"study_design_scores_gemma":[0.00001098473,0.000004658511,0.0001592918,0.00003361555,0.000005795185,0.00008236245,0.001087147,0.0004480344,0.00009214196,0.9735502,0.02451787,0.000007918027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04703158,0.006515554,0.3242332,0.03431808,0.001897398,0.0001027007,0.00126273,0.000540314,0.5840985],"genre_scores_gemma":[0.8947806,0.00241643,0.07371886,0.004005196,0.00179125,0.0002517362,0.001208878,0.0006281437,0.02119889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01010294,"threshold_uncertainty_score":0.03379768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509765901289863,"score_gpt":0.2859396778483146,"score_spread":0.230842018835416,"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."}}