{"id":"W6931827947","doi":"10.5683/sp/oahmb5","title":"GTA LGR/AMR Truck Campaign July 24 - August 4, 2017","year":2017,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University; University of Toronto","funders":"","keywords":"Analyser; Truck; Data collection; Weather station","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001067694,0.001459621,0.001649224,0.0006565075,0.0008559895,0.0009781257,0.005235235,0.001623439,0.0009693038],"category_scores_gemma":[0.001386735,0.001398983,0.0005943444,0.0002003407,0.0006967401,0.0005690422,0.0008223639,0.001667235,0.02744614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005073218,"about_ca_system_score_gemma":0.0009009506,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2059926,"about_ca_topic_score_gemma":0.0938346,"domain_scores_codex":[0.9939138,0.0003702311,0.0009534457,0.001655277,0.001584837,0.001522467],"domain_scores_gemma":[0.9871261,0.0001730692,0.00189631,0.00970375,0.000333748,0.0007669945],"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":[0.0001405521,0.0001742242,0.0000172396,0.0002281066,0.0003285666,0.0008075479,0.00003945968,0.000001862027,0.000008048577,0.00004085855,0.9980057,0.0002077576],"study_design_scores_gemma":[0.0010367,0.0001100908,0.001088968,0.0002898855,0.0008057188,0.000123566,0.00001447506,0.00000307576,0.0000180167,0.0002187369,0.9946986,0.001592186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000215101,0.000408152,0.000001645598,0.00008735839,0.001445604,0.0009151305,0.9546703,0.0004133487,0.04205633],"genre_scores_gemma":[0.000002082505,0.0004511954,0.0001305874,0.0002818669,0.002699592,0.0002065358,0.9882653,0.0004588703,0.007503905],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.112158,"threshold_uncertainty_score":0.999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04418240827626262,"score_gpt":0.3172516295373689,"score_spread":0.2730692212611062,"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."}}