{"id":"W4220761027","doi":"10.1038/d41586-022-00792-2","title":"Lessons from the COVID data wizards","year":2022,"lang":"en","type":"article","venue":"Nature","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Data science; 2019-20 coronavirus outbreak; Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); World Wide Web; Medicine; Virology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004689539,0.00008866642,0.0001968759,0.00001897559,0.0001863315,0.00001673028,0.0008421278,0.0001513943,0.002212588],"category_scores_gemma":[0.0008796316,0.00005857287,0.0000541978,0.0002136901,0.00008586547,0.000054687,0.001004067,0.002185707,0.00005981071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005671526,"about_ca_system_score_gemma":0.0002291482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000137657,"about_ca_topic_score_gemma":0.00008170673,"domain_scores_codex":[0.998717,0.0001747911,0.000105332,0.0003426522,0.0005162828,0.0001439045],"domain_scores_gemma":[0.9977129,0.0002730096,0.00005126731,0.001835459,0.00003474355,0.00009258508],"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":[0.0006067893,0.00006359859,0.008074618,0.000008187244,0.0001033703,0.0001146857,0.0001013469,0.000006685795,0.00006642166,0.001253993,0.986138,0.003462275],"study_design_scores_gemma":[0.002480812,0.00001369309,0.02509284,0.000006984573,0.00008502483,0.00002275513,0.000119409,0.0003960771,0.000007110914,0.0004117081,0.9712933,0.00007031448],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09508777,0.06185276,0.000249368,0.6239969,0.004364829,0.002125636,0.1791299,0.0009202635,0.03227261],"genre_scores_gemma":[0.9333508,0.00007822549,0.0003376724,0.04747052,0.0004875418,0.00002749086,0.01709078,0.00002279944,0.001134186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.838263,"threshold_uncertainty_score":0.9986995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04642288869653496,"score_gpt":0.3623665615561117,"score_spread":0.3159436728595768,"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."}}