{"id":"W4401033982","doi":"10.26717/bjstr.2023.51.008062","title":"Artificial Intelligence and Medical Oxygen","year":2023,"lang":"en","type":"article","venue":"Biomedical Journal of Scientific & Technical Research","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Oxygen; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Medicine; Virology; Chemistry; Pathology; Infectious disease (medical specialty); Disease; Outbreak","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":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01821576,0.000112025,0.0003661374,0.001865779,0.0003863637,0.00006896299,0.000584307,0.0005548808,0.0002552314],"category_scores_gemma":[0.01423255,0.00008029778,0.0001067206,0.003946297,0.003840343,0.00009163217,0.0004573506,0.002330823,0.0002180008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001250312,"about_ca_system_score_gemma":0.001245335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009614881,"about_ca_topic_score_gemma":0.000004128802,"domain_scores_codex":[0.9934302,0.0002217229,0.0009591642,0.0003693705,0.004267721,0.0007518196],"domain_scores_gemma":[0.9962679,0.001102438,0.0001103593,0.0003495789,0.0008139769,0.001355725],"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":[0.0002824944,0.000446768,0.002876355,0.0001583584,0.00003654691,0.002233005,0.0001297332,1.348182e-7,0.03409223,0.004575484,0.01837906,0.9367898],"study_design_scores_gemma":[0.002814984,0.02256881,0.1367546,0.0117207,0.0002285838,0.01617886,0.005685982,0.005556738,0.07484471,0.187755,0.5344576,0.001433415],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9519879,0.0005236307,0.001440318,0.04400856,0.001297981,0.0002965133,0.000004451276,0.000168779,0.0002719102],"genre_scores_gemma":[0.9974326,0.00046788,0.00122645,0.00006884733,0.0005588069,0.000007819832,0.000006671688,0.00001372512,0.0002171824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9353564,"threshold_uncertainty_score":0.9999709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2905993628104039,"score_gpt":0.5024645862450838,"score_spread":0.2118652234346799,"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."}}