{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004967592,0.002442117,0.001213438,0.003408581,0.003000847,0.001634849,0.003156551,0.001604901,0.01836266],"category_scores_gemma":[0.002519029,0.0003767514,0.001124059,0.005494186,0.0006051406,0.0007649561,0.00138873,0.001642207,0.01388455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007915563,"about_ca_system_score_gemma":0.01290107,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9346429,"about_ca_topic_score_gemma":0.9752257,"domain_scores_codex":[0.9986761,0.0000819218,0.00006319569,0.0002889064,0.0005620248,0.0003277694],"domain_scores_gemma":[0.9975449,0.0001057288,0.00007681818,0.0002570314,0.001797301,0.0002181366],"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":[0.0001686139,0.00008134755,0.009511722,0.0003124872,0.0001115866,0.0001501045,0.00007762259,0.001442421,0.0004565572,0.0006324225,0.9736623,0.01339282],"study_design_scores_gemma":[0.0001435201,0.00005677038,0.08088777,0.0003745148,0.0001178127,0.000249134,0.0008795707,0.00771026,0.001428149,0.0007585708,0.9071864,0.000207542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004506965,0.0002460481,0.000361875,0.0001678024,0.0001029371,0.0001227442,0.9888954,0.0005891587,0.005006972],"genre_scores_gemma":[0.007141479,0.0001405539,0.0008191592,0.00007597129,0.00001690726,0.00009826401,0.9893277,0.00005093671,0.002328946],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06535709,"threshold_uncertainty_score":0.1314839,"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."}}