{"id":"W4206446151","doi":"10.2196/preprints.25935","title":"Collaborating in the Time of COVID-19: The Scope and Scale of Innovative Responses to a Global Pandemic (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Crowdsourcing; Pandemic; Data sharing; Blueprint; Citizen science; Infographic; Data science; Political science; Public relations; Knowledge management; Coronavirus disease 2019 (COVID-19); World Wide Web; Computer science; Engineering","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":[0.008168722,0.0003551531,0.0003329342,0.001261987,0.004344498,0.01142469,0.0007338949,0.002681296,0.0162303],"category_scores_gemma":[0.02517366,0.0003445727,0.0006155543,0.001495735,0.003805013,0.007706972,0.00566077,0.002889823,0.003563755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001742171,"about_ca_system_score_gemma":0.003087033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001696098,"about_ca_topic_score_gemma":0.002095072,"domain_scores_codex":[0.9961811,0.001902304,0.0001437308,0.0004882567,0.0008431366,0.0004414672],"domain_scores_gemma":[0.9784278,0.01377801,0.001154314,0.001649073,0.002801483,0.002189336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004273976,0.0001404126,0.01486738,0.001592488,0.0000846276,0.001281328,0.05489939,0.001880739,0.00510352,0.1368678,0.5074614,0.2753936],"study_design_scores_gemma":[0.00003201391,0.0001382751,0.01279063,0.001127945,0.00005130456,0.0003210943,0.0426204,0.001317547,0.002216588,0.06058853,0.8787048,0.00009092593],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.217508,0.02720235,0.04284324,0.3021463,0.03339764,0.0005035932,0.002644785,0.002168838,0.3715852],"genre_scores_gemma":[0.8638089,0.01803897,0.02477425,0.01932154,0.00939193,0.0005051622,0.001781372,0.001401507,0.06097628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0162303,"threshold_uncertainty_score":0.05429572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0600059443214734,"score_gpt":0.3947821297067819,"score_spread":0.3347761853853086,"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."}}