{"id":"W4226427181","doi":"10.2196/preprints.38170","title":"Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Bluetooth; Global Positioning System; Computer science; Tracing; Contact tracing; Real-time computing; Remote sensing; Geography; Telecommunications; World Wide Web; Wireless; Operating system; Coronavirus disease 2019 (COVID-19); Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001162275,0.000637344,0.0009884328,0.0001977954,0.0004154771,0.001920702,0.004572825,0.0002041606,0.00004422226],"category_scores_gemma":[0.0003292904,0.0006871308,0.0001532049,0.00039771,0.00005859356,0.002618517,0.02126588,0.001383192,0.00001221531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004050721,"about_ca_system_score_gemma":0.0004168602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292986,"about_ca_topic_score_gemma":0.0003289986,"domain_scores_codex":[0.9944788,0.0003023923,0.0008911744,0.002859552,0.0008414674,0.000626619],"domain_scores_gemma":[0.9933365,0.001459382,0.0004933519,0.0044036,0.0001269942,0.000180175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002132564,0.0001354141,0.04234501,0.0005437973,0.0001556021,0.00003149089,0.003088876,0.9320214,0.00002628809,0.01808219,0.00009137464,0.003457191],"study_design_scores_gemma":[0.0004831621,0.00003193176,0.03188438,0.0001111814,0.0000129985,0.00001283682,0.000167602,0.9627628,0.00001438627,0.001792315,0.001938526,0.0007878706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05107557,0.0004033881,0.9208788,0.0003143387,0.0006246148,0.001094616,0.00007171521,0.000908863,0.02462813],"genre_scores_gemma":[0.9533398,0.00004664779,0.04553684,0.0004321191,0.0001208407,0.00006343696,0.0002917201,0.00005047375,0.0001181127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9022642,"threshold_uncertainty_score":0.999558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240478009595633,"score_gpt":0.3349310614381294,"score_spread":0.2108832604785661,"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."}}