{"id":"W6894240429","doi":"10.5683/sp3/uw4vtc","title":"Replication Data for: Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Statistical Sciences Institute","keywords":"Hotspot (geology); Autoregressive model; Coronavirus disease 2019 (COVID-19); Predictive modelling; Time series; Key (lock)","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002208056,0.0006075634,0.0006791999,0.0006750392,0.0005191396,0.0002547921,0.001671107,0.0006984558,0.00003573783],"category_scores_gemma":[0.01135471,0.0006518213,0.0001021889,0.0006903411,0.0002631945,0.0003524905,0.001383713,0.000590169,0.000006001479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007282267,"about_ca_system_score_gemma":0.002161094,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.087227,"about_ca_topic_score_gemma":0.08248439,"domain_scores_codex":[0.9950008,0.0002114966,0.0008790777,0.002625748,0.0006619979,0.0006208466],"domain_scores_gemma":[0.9880363,0.0005517828,0.001148412,0.009348253,0.0001886386,0.0007266345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008417995,0.00007909523,0.0002420953,0.0007421835,0.0002301856,0.00003196979,0.00005827595,0.00000344509,0.00003164832,0.00001749288,0.996615,0.001864408],"study_design_scores_gemma":[0.0008628892,0.00008802523,0.0005870506,0.0001025272,0.0007649916,0.0001180498,0.000128877,0.000853597,0.00002952592,0.0001247657,0.995802,0.0005376809],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002904142,0.0004710602,0.0001364005,0.000609527,0.0002502918,0.001833115,0.9963455,0.0002743597,0.0000507378],"genre_scores_gemma":[0.00002090826,0.000346577,0.000645905,0.0007812615,0.0009050625,0.0007355199,0.9963386,0.0001881402,0.00003800374],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009146656,"threshold_uncertainty_score":0.9995933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07727804651807914,"score_gpt":0.338100774888534,"score_spread":0.2608227283704548,"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."}}