{"id":"W7018339609","doi":"","title":"Deep Learning and Spatial Statistics for Determining Road Surface Condition","year":2019,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Road surface; Deep learning; Sample (material); Snow; Christian ministry; Process (computing); Set (abstract data type); Spatial analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003609384,0.0006320504,0.0004877795,0.00195881,0.0002039842,0.0007822019,0.0007191666,0.0005316688,0.001592448],"category_scores_gemma":[0.001362463,0.0002683764,0.0005153084,0.001943534,0.0004164615,0.0009681724,0.0007093215,0.0006950192,0.0008117395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008078861,"about_ca_system_score_gemma":0.0008779078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01196305,"about_ca_topic_score_gemma":0.01645911,"domain_scores_codex":[0.9997014,0.00005008042,0.00001784428,0.00009916311,0.00009605693,0.00003530493],"domain_scores_gemma":[0.9995428,0.0001535805,0.00009061724,0.00007151289,0.0001183155,0.0000230985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008607958,0.000126753,0.0210581,0.0001846832,0.0001071891,0.0001145505,0.00008816076,0.3275505,0.01360525,0.01619232,0.007743766,0.6131426],"study_design_scores_gemma":[0.000002759636,0.0000161276,0.005088576,0.0000126792,0.00001359948,0.00003093366,0.00002740461,0.9797344,0.003970946,0.008203792,0.002884283,0.0000145146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0570855,0.001221698,0.9320589,0.0005219219,0.00009196655,0.00003646915,0.001710676,0.002657277,0.00461559],"genre_scores_gemma":[0.7549821,0.001454678,0.2337732,0.0002549598,0.0002087683,0.00009196759,0.003214455,0.0001934207,0.005826375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01196305,"threshold_uncertainty_score":0.02378684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005794446436642444,"score_gpt":0.2002943728820416,"score_spread":0.1944999264453991,"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."}}