{"id":"W4239628373","doi":"10.7287/peerj.preprints.1986","title":"CATALISE: a multinational and multidisciplinary Delphi consensus study. Identifying language impairments in children","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Delphi Technique in Research","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Delphi method; Statement (logic); Set (abstract data type); Psychology; Delphi; Scale (ratio); Multidisciplinary approach; Relevance (law); Medical education; Psychological intervention; Panel discussion; Multinational corporation; Medicine; Family medicine; Political science; Psychiatry; Linguistics; Computer science; Law; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00303247,0.0002385749,0.0002991247,0.0004941872,0.0003392623,0.0001978916,0.0006780369,0.0003058063,0.0001798302],"category_scores_gemma":[0.0006638436,0.0002090637,0.00006777084,0.0002207451,0.0004547946,0.0001110224,0.002601123,0.000563411,0.00002738485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004005815,"about_ca_system_score_gemma":0.000434405,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02858279,"about_ca_topic_score_gemma":0.01278314,"domain_scores_codex":[0.9966269,0.0006393362,0.0004457167,0.0007222148,0.001049442,0.0005164222],"domain_scores_gemma":[0.9986774,0.0004116232,0.0001643638,0.0004069743,0.0001644278,0.0001752172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003483575,0.0005915517,0.9504355,0.00004212574,0.00008549193,0.0003041941,0.04283394,0.000003989361,0.0001589824,0.0003990227,0.0008152639,0.004295064],"study_design_scores_gemma":[0.001443734,0.00005226189,0.9523149,0.0002855049,0.00001987829,0.00002015202,0.03983352,0.0001487057,0.00006270545,0.005295748,0.00002082262,0.0005021321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907606,0.0002001028,0.00008922109,0.001419124,0.0001501021,0.003159991,0.0001292504,0.0002121389,0.00387942],"genre_scores_gemma":[0.9962404,0.00005891814,0.001579343,0.00001490654,0.000147832,0.0003554235,0.0000392699,0.00002771705,0.001536127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01579965,"threshold_uncertainty_score":0.977886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0827397129442484,"score_gpt":0.4759211568847165,"score_spread":0.3931814439404681,"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."}}