{"id":"W6961357248","doi":"10.14473/csda/ayv5il","title":"Život během pandemie (Život k nezaplacení), spojený soubor vlny 1–44","year":2023,"lang":"cs","type":"dataset","venue":"Czech Social Science Data Archive","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","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":[0.004196402,0.0003524413,0.0005166405,0.001564897,0.002570638,0.00467228,0.0009873911,0.001420532,0.01956335],"category_scores_gemma":[0.01106873,0.0004647406,0.000856012,0.002137207,0.00176954,0.002704173,0.005373358,0.002601113,0.002328116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003802447,"about_ca_system_score_gemma":0.01273983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05764844,"about_ca_topic_score_gemma":0.09856846,"domain_scores_codex":[0.9954235,0.001889811,0.0003842333,0.0005669385,0.001005628,0.0007298153],"domain_scores_gemma":[0.9951585,0.001001345,0.001257381,0.0003628305,0.001160862,0.001059149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007930862,0.0007078573,0.5722428,0.004259317,0.0006883786,0.001112701,0.02931741,0.0004447038,0.002278696,0.04669238,0.05597414,0.2854885],"study_design_scores_gemma":[0.00007653527,0.0006078038,0.6071184,0.003798273,0.0003413158,0.001078369,0.03588943,0.0002111198,0.001625841,0.009407175,0.3397425,0.0001033204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5698705,0.03371952,0.008155754,0.08746627,0.002281391,0.001458041,0.01557988,0.0002576335,0.281211],"genre_scores_gemma":[0.884738,0.02191689,0.006982322,0.009686144,0.0005933081,0.001179394,0.004487257,0.0001003693,0.07031641],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.05764844,"threshold_uncertainty_score":0.1146258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05646408641336131,"score_gpt":0.3239297941534267,"score_spread":0.2674657077400653,"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."}}