{"id":"W3036790661","doi":"10.59275/j.melba.2020-48g7","title":"COVID-19 Image Data Collection: Prospective Predictions are the Future","year":2020,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Université de Montréal","funders":"Compute Canada; Canadian Institute for Advanced Research","keywords":"Metadata; Coronavirus disease 2019 (COVID-19); Data collection; Computer science; Resource (disambiguation); Data science; Code (set theory); Intensive care unit; Medicine; Medical physics; Medical emergency; Information retrieval; Artificial intelligence; Intensive care medicine; World Wide Web; Disease; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004512691,0.0004575744,0.0009080207,0.0003774221,0.001216358,0.0002025249,0.001715946,0.0002216547,0.0001731148],"category_scores_gemma":[0.01748254,0.0002569855,0.0004460812,0.0007705056,0.0008339927,0.0001740488,0.001841085,0.006010998,0.000008304047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009289461,"about_ca_system_score_gemma":0.003226576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002509303,"about_ca_topic_score_gemma":0.00002311892,"domain_scores_codex":[0.9959441,0.0007171099,0.001041581,0.0005832785,0.001270075,0.0004438998],"domain_scores_gemma":[0.9930391,0.002565827,0.001816355,0.001123461,0.0006204338,0.0008348483],"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":[0.002559711,0.0006214273,0.01668695,0.002203318,0.001872276,0.0004400926,0.004844059,0.002492706,0.000611301,0.00004042862,0.9600375,0.007590245],"study_design_scores_gemma":[0.003483721,0.000479906,0.005006555,0.0009974821,0.002848606,0.002451492,0.002067909,0.2043349,0.00002819263,0.001304397,0.7766981,0.0002987169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0006230681,0.005623883,0.1566202,0.8329017,0.002362306,0.001366926,0.0002634614,0.0001893232,0.00004918189],"genre_scores_gemma":[0.6872095,0.00953888,0.03290452,0.2113638,0.05364275,0.0003064842,0.002753128,0.0007582913,0.001522655],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.6865864,"threshold_uncertainty_score":0.9999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03905790863781753,"score_gpt":0.3697702149244061,"score_spread":0.3307123062865886,"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."}}