{"id":"W7051984294","doi":"","title":"Pandemic Patterns: California is Seeing Fewer Entrances and More Exits","year":2022,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Plasma Diagnostics and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bay; Pandemic; Quarter (Canadian coin); Population; Coronavirus disease 2019 (COVID-19)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003147616,0.0002804117,0.0002590068,0.0006812504,0.0007606964,0.001360478,0.0004413579,0.0005059675,0.008451004],"category_scores_gemma":[0.001084857,0.0002315906,0.0003603902,0.001376587,0.0002419928,0.001092478,0.0005744182,0.0009987487,0.000972014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001192147,"about_ca_system_score_gemma":0.001719484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1723974,"about_ca_topic_score_gemma":0.30798,"domain_scores_codex":[0.9996259,0.00003057578,0.00003482433,0.0001216858,0.00009936947,0.00008762963],"domain_scores_gemma":[0.9990356,0.00006896523,0.0003495266,0.00005026638,0.0003486506,0.0001469128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002639427,0.000125966,0.6943426,0.0005632163,0.0001513546,0.0002210512,0.001597254,0.0008365928,0.0006217665,0.001544564,0.2107068,0.08902493],"study_design_scores_gemma":[0.00003259236,0.00006109547,0.9413608,0.0002282162,0.00005418342,0.0002209588,0.002401604,0.0003700202,0.0001543914,0.0003211981,0.0547593,0.00003570467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7249086,0.008320002,0.001280189,0.02230166,0.001224893,0.0002437674,0.08639438,0.0005515005,0.1547751],"genre_scores_gemma":[0.9135665,0.009559842,0.001976031,0.005739238,0.0005014911,0.0001402203,0.05148883,0.0001163743,0.0169115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1723974,"threshold_uncertainty_score":0.3427879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0100740420974189,"score_gpt":0.1963133934067353,"score_spread":0.1862393513093164,"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."}}