{"id":"W3004135102","doi":"10.1177/0278364920979368","title":"Canadian Adverse Driving Conditions dataset","year":2020,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":210,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adverse weather; Lidar; Frame (networking); Ground truth; Scale (ratio); Tracking (education)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000326167,0.00005045695,0.00007729544,0.0001161704,0.0001349639,0.00006485691,0.0009432341,0.00001835701,0.0002512788],"category_scores_gemma":[0.0003387651,0.00003591664,0.00004428515,0.0001555998,0.0001501208,0.0001306034,0.0001965406,0.000601874,0.00008755951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205984,"about_ca_system_score_gemma":0.0002569353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001708807,"about_ca_topic_score_gemma":0.0005713063,"domain_scores_codex":[0.9989609,0.00004542014,0.0001840101,0.00006860679,0.0005454008,0.0001956631],"domain_scores_gemma":[0.9987897,0.0002994066,0.0000735291,0.0001165931,0.0005590105,0.0001618122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000810285,0.0001245252,0.02555262,0.000004089073,0.0007727771,0.0004033617,0.0006009171,0.2152025,0.006239216,0.5065618,0.2331433,0.01131392],"study_design_scores_gemma":[0.002921546,0.0006994074,0.008935661,0.0003536599,0.0001257941,0.0002012607,0.0125433,0.08429592,0.01500316,0.500084,0.3740135,0.000822734],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1862821,0.00008966359,0.08891812,0.709451,0.001255695,0.000410206,0.00153888,0.00005035169,0.01200405],"genre_scores_gemma":[0.9945552,0.000006210473,0.004654216,0.0002698964,0.0004053453,5.333952e-7,0.00005429641,0.000006992137,0.00004726481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8082732,"threshold_uncertainty_score":0.2751327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08187123409436013,"score_gpt":0.3972546324907107,"score_spread":0.3153833983963506,"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."}}