{"id":"W6961380015","doi":"10.14473/csda/vroqoi","title":"Život během pandemie (Život k nezaplacení), spojený soubor vlny 1–45","year":2023,"lang":"cs","type":"dataset","venue":"Czech Social Science Data Archive","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Coronavirus disease 2019 (COVID-19)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004236113,0.0003372737,0.0004961509,0.001563138,0.002567217,0.004714844,0.0009529023,0.001386901,0.01913538],"category_scores_gemma":[0.01132863,0.0004569817,0.0008560927,0.002096165,0.001758333,0.002731811,0.005301807,0.002492932,0.002320605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003729574,"about_ca_system_score_gemma":0.01241215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06089611,"about_ca_topic_score_gemma":0.09825914,"domain_scores_codex":[0.9953282,0.001893415,0.0003827045,0.0005642129,0.001034931,0.0007966782],"domain_scores_gemma":[0.9949313,0.00100363,0.001304248,0.0003709488,0.001214516,0.001175357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007417125,0.0006502045,0.6004788,0.003603832,0.0006127926,0.0009945505,0.02904075,0.0003957924,0.002021193,0.04242999,0.05386718,0.2651634],"study_design_scores_gemma":[0.00006915487,0.0005516676,0.6268297,0.00332183,0.0003104101,0.0009971659,0.03456978,0.0001850366,0.001464995,0.008300279,0.3233047,0.00009526913],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5819768,0.03190028,0.006806402,0.08362997,0.002148501,0.001325226,0.01457592,0.0002393243,0.2773976],"genre_scores_gemma":[0.8927367,0.02036417,0.006004881,0.009264021,0.0005494604,0.001053022,0.004310994,0.00009224763,0.06562448],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.06089611,"threshold_uncertainty_score":0.1210833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2709834127029522,"score_gpt":0.4613774402786523,"score_spread":0.1903940275757001,"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."}}