{"id":"W3215495643","doi":"10.21203/rs.3.rs-1098637/v1","title":"The Blursday Database: Individuals’ Temporalities in Covid Times","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Media Influence and Health","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Université Laval","funders":"H2020 European Research Council; Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; University of Tokyo; Agence Nationale de la Recherche","keywords":"Temporalities; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Data science; Database; Virology; Political science; Biology; Medicine; Infectious disease (medical specialty); Outbreak; Disease; Internal medicine","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":["sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005913163,0.000262839,0.000414064,0.0004302242,0.001305826,0.002244358,0.0009859693,0.0001972995,0.005726407],"category_scores_gemma":[0.001196206,0.0001840234,0.0001237746,0.0001381584,0.00110199,0.0003102537,0.00155488,0.002808521,0.0002900255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002501187,"about_ca_system_score_gemma":0.00259336,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01335149,"about_ca_topic_score_gemma":0.03166297,"domain_scores_codex":[0.9948716,0.001298988,0.0005741907,0.0005973011,0.001635488,0.001022453],"domain_scores_gemma":[0.9960877,0.001746724,0.0001291657,0.001177647,0.0006011094,0.0002576432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000676373,0.0003040673,0.01936099,0.005975478,0.0001410119,0.0003223739,0.2753195,0.00003538247,0.000005550972,0.4690952,0.2240595,0.005313333],"study_design_scores_gemma":[0.0002639888,0.00007949184,0.001391049,0.002455493,0.000007236891,0.000001564199,0.1386532,0.00003954586,0.00002176188,0.008436147,0.8483676,0.0002829665],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6822398,0.07270215,0.000006383912,0.04878398,0.004669012,0.005843545,0.003696775,0.0003447207,0.1817136],"genre_scores_gemma":[0.9468817,0.01746654,0.00007330539,0.0008098496,0.003054169,0.001384165,0.002578112,0.00008545643,0.02766669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.624308,"threshold_uncertainty_score":0.9999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.222791287456232,"score_gpt":0.4399860091597512,"score_spread":0.2171947217035193,"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."}}