{"id":"W7099035965","doi":"","title":"SUMMARY","year":2015,"lang":"en","type":"article","venue":"","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mechanical ventilation; Intensive care unit; Intensive care; Anticipation (artificial intelligence); Life support; Ventilation (architecture); Artificial ventilation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001304702,0.0005182381,0.0004000308,0.001255029,0.001472035,0.003891476,0.001075082,0.001363481,0.4000997],"category_scores_gemma":[0.004970478,0.0001843879,0.000398855,0.001283535,0.0003798753,0.001786382,0.002031744,0.0013552,0.2352307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386001,"about_ca_system_score_gemma":0.003422875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002810573,"about_ca_topic_score_gemma":0.003215595,"domain_scores_codex":[0.9986791,0.0001921587,0.0001209725,0.0002061796,0.0005797601,0.0002217674],"domain_scores_gemma":[0.9968918,0.0002808969,0.0001418996,0.0002897477,0.001636345,0.0007593581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001064971,0.00005537264,0.001907432,0.0002070217,0.000008213261,0.000334152,0.0002750543,0.00009912693,0.0003677393,0.01523771,0.8001718,0.1812298],"study_design_scores_gemma":[0.000006813594,0.00002221438,0.001067257,0.00009746596,0.000002611164,0.0002168326,0.0001305504,0.00003375164,0.0001075213,0.001372195,0.9969381,0.000004826265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006547094,0.0055031,0.004255087,0.04474542,0.01610498,0.0004442255,0.01320983,0.001264274,0.9079259],"genre_scores_gemma":[0.02728269,0.004314546,0.003094166,0.01415067,0.003119567,0.0002322323,0.01022212,0.0003788386,0.9372052],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5999004,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0459029980435336,"score_gpt":0.2271453448375257,"score_spread":0.1812423467939921,"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."}}