{"id":"W47286948","doi":"10.1007/978-3-642-22218-4_28","title":"COPE: Childhood Obesity Prevention [Knowledge] Enterprise","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Ontology; Childhood obesity; Obesity; Computer science; Overweight; Domain (mathematical analysis); Work (physics); Domain knowledge; Knowledge management; Discipline; Medicine; Engineering; Social science; Sociology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003515496,0.000288276,0.0002637476,0.0001480493,0.0001055983,0.00004557274,0.0007972154,0.0004866013,0.00004479276],"category_scores_gemma":[0.00008803553,0.0002486909,0.000121074,0.00008630982,0.0007135192,0.000004589394,0.000621714,0.0003440844,0.00004956489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003930328,"about_ca_system_score_gemma":0.0003030751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004280845,"about_ca_topic_score_gemma":0.00009030745,"domain_scores_codex":[0.9983687,0.00002729926,0.0002478962,0.0008141534,0.0002141744,0.0003278159],"domain_scores_gemma":[0.9990818,0.00003095331,0.0001270208,0.0005635025,0.00009150051,0.0001052387],"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":[0.00001784396,0.0001763659,0.0003792106,0.00003350293,0.00002845239,0.00001352424,0.0003246917,0.00004527395,0.001291419,0.0001462718,0.0002930393,0.9972504],"study_design_scores_gemma":[0.005056723,0.009432158,0.02819333,0.004590872,0.0002805269,0.000663524,0.000003338734,0.005596021,0.210267,0.3294749,0.3991693,0.007272352],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004704759,0.003174706,0.9822896,0.00007986717,0.001164845,0.0002387285,0.000009350383,0.00004393151,0.008294207],"genre_scores_gemma":[0.9083062,0.000803785,0.08016489,0.0007923492,0.00161606,0.00001662655,0.00009272587,0.00006598643,0.008141424],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9899781,"threshold_uncertainty_score":0.9999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552163708837703,"score_gpt":0.2594462778941531,"score_spread":0.2439246408057761,"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."}}