{"id":"W3209765266","doi":"10.5220/0010688300003064","title":"COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach","year":2021,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Modular programming; Computer science; Comet; Ontology; Extraction (chemistry); Software engineering; Programming language; Astrobiology; Chemistry; Chromatography","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":[0.001556365,0.001599571,0.001303459,0.006889686,0.001111,0.003113623,0.001744305,0.001113727,0.006565637],"category_scores_gemma":[0.005681601,0.0009489653,0.00274523,0.003766004,0.0006146737,0.004447594,0.003546532,0.001923317,0.004261997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009289857,"about_ca_system_score_gemma":0.002014471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003351245,"about_ca_topic_score_gemma":0.006544784,"domain_scores_codex":[0.9986863,0.0001102535,0.0001526067,0.000283185,0.0006887109,0.0000789851],"domain_scores_gemma":[0.997875,0.0007425529,0.0001915984,0.0005195936,0.0005559879,0.0001152196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003642881,0.0005274421,0.009122666,0.003106726,0.001103474,0.00185454,0.001314345,0.004860035,0.04401462,0.04595728,0.1618997,0.7258749],"study_design_scores_gemma":[0.000353024,0.0001887688,0.01265028,0.0009899301,0.0009151385,0.004568113,0.0007990571,0.1394974,0.08633307,0.06876606,0.6845909,0.0003481394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009367866,0.0005171545,0.893716,0.0004461745,0.0002046758,0.0005180981,0.007965793,0.07959513,0.007668979],"genre_scores_gemma":[0.06355245,0.0008487905,0.8599446,0.0008525295,0.0001127461,0.0007001971,0.05175385,0.01400652,0.008228282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006889686,"threshold_uncertainty_score":0.02196419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648683924671082,"score_gpt":0.2681671955985758,"score_spread":0.241680356351865,"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."}}