{"id":"W4318147167","doi":"10.1109/bigdata55660.2022.10020867","title":"Emergence of an Autonomous Vehicle Secondary Data Market for Breakthrough Applications","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Data science; Cloud computing; Variety (cybernetics); Architecture; Data collection; Key (lock); Telecommunications; Systems engineering; Artificial intelligence; Engineering; Computer security; Geography","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":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003000716,0.000170404,0.0002477839,0.0001831933,0.001017204,0.0001608357,0.01061071,0.00006404257,0.01173459],"category_scores_gemma":[0.0004078787,0.0002016068,0.00004758112,0.0004540755,0.0003394316,0.001158606,0.002201926,0.0003233613,0.00003449031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001004498,"about_ca_system_score_gemma":0.001541866,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008235564,"about_ca_topic_score_gemma":0.03286694,"domain_scores_codex":[0.9964315,0.0003956484,0.0005662617,0.001276815,0.001042249,0.000287554],"domain_scores_gemma":[0.9942868,0.0003520852,0.0003636292,0.00454926,0.000308872,0.000139407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00018471,0.001140142,0.0003492754,0.00004671112,0.0002899245,0.000004239989,0.001094193,0.0001600127,0.0006651166,0.02743535,0.1966378,0.7719926],"study_design_scores_gemma":[0.0004323911,0.00009572615,0.0004440402,0.00001390526,0.00009179275,0.000001483805,0.006467714,0.1713104,0.00006359342,0.003461242,0.8173012,0.0003165392],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01212628,0.0002690576,0.1271326,0.01623726,0.006944743,0.003064519,0.7705986,0.0002949914,0.06333195],"genre_scores_gemma":[0.8494418,0.0002119051,0.0008106812,0.0005258239,0.001168295,0.0003189979,0.1455466,0.00002075382,0.001955043],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.8373156,"threshold_uncertainty_score":0.9983687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3288503865665113,"score_gpt":0.4098341532725872,"score_spread":0.08098376670607593,"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."}}