{"id":"W3018850096","doi":"10.1101/2020.04.17.20068460","title":"CAN-NPI: A Curated Open Dataset of Canadian Non-Pharmaceutical Interventions in Response to the Global COVID-19 Pandemic","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Trillium Health Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Pandemic; Psychological intervention; Coronavirus disease 2019 (COVID-19); Government (linguistics); Intervention (counseling); Geography; Business; Computer science; Data science; Medicine; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.003041816,0.000840484,0.0007263398,0.003731612,0.002062581,0.00201385,0.002594722,0.001486915,0.01730303],"category_scores_gemma":[0.02573864,0.0004294397,0.001169199,0.008713872,0.0007224187,0.0004836561,0.001836247,0.001693942,0.004597954],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02566219,"about_ca_system_score_gemma":0.07791035,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9749078,"about_ca_topic_score_gemma":0.9815556,"domain_scores_codex":[0.9968321,0.0006081439,0.0002254028,0.0005346749,0.001216436,0.0005832349],"domain_scores_gemma":[0.9882501,0.002954315,0.0008931096,0.001437114,0.005172673,0.001292711],"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":[0.0002412524,0.00008927449,0.01385443,0.0009062022,0.000157569,0.00004679311,0.0003038418,0.00278648,0.0001735093,0.004034024,0.965433,0.01197354],"study_design_scores_gemma":[0.0006338975,0.00005723868,0.1115042,0.001107851,0.0001603485,0.00005616211,0.0008709668,0.004192528,0.0006276552,0.002308745,0.8783302,0.0001501774],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002604854,0.0001784652,0.0004409345,0.0006473123,0.0000416024,0.0001333595,0.9928145,0.0002146349,0.002924228],"genre_scores_gemma":[0.01753176,0.0002697105,0.004345261,0.0005013944,0.0000402706,0.0006581394,0.9728628,0.0001294968,0.003661253],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9974053,"threshold_uncertainty_score":0.1861931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8536768552349719,"score_gpt":0.7351941194482872,"score_spread":0.1184827357866848,"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."}}