{"id":"W6958569414","doi":"10.6084/m9.figshare.16960363.v1","title":"Additional file 2 of Quantifying contact patterns in response to COVID-19 public health measures in Canada","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Public health; Data collection; Public access; MEDLINE; Government (linguistics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000862351,0.0007515051,0.0007904359,0.004814498,0.002204605,0.002368717,0.001752843,0.0007703655,0.766671],"category_scores_gemma":[0.02301951,0.0004999149,0.0008048019,0.01108705,0.0003788818,0.001221441,0.00115645,0.0007675366,0.08972849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007652407,"about_ca_system_score_gemma":0.01834085,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8336543,"about_ca_topic_score_gemma":0.861977,"domain_scores_codex":[0.9989854,0.00008122157,0.0001277853,0.0001699915,0.0003915193,0.0002440232],"domain_scores_gemma":[0.9828499,0.00728024,0.0008109141,0.001108536,0.007275527,0.0006749014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00004413501,0.00001751711,0.002370906,0.0005350899,0.00001295331,0.00001990435,0.00008463982,0.0002338113,0.00002028843,0.0006105405,0.9909745,0.005075792],"study_design_scores_gemma":[0.0005565292,0.00003684726,0.05513376,0.001693857,0.00007314827,0.0001319209,0.001020001,0.001186276,0.0002986051,0.002650534,0.9371185,0.00009996953],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0001819471,0.0000102552,0.0000748634,0.00005770119,0.00001236609,0.000047897,0.9964959,0.0001203832,0.002998636],"genre_scores_gemma":[0.01355789,0.0001766888,0.002147062,0.0002765285,0.00004097865,0.0007555104,0.9522536,0.0005806389,0.03021101],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.766671,"threshold_uncertainty_score":0.3346507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1839904502744928,"score_gpt":0.3219812515618475,"score_spread":0.1379908012873547,"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."}}