{"id":"W2884161042","doi":"10.1109/itsc.2018.8569519","title":"Unlimited Road-scene Synthetic Annotation (URSA) Dataset","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Annotation; Ground truth; Synthetic data; Limiting; Artificial intelligence; Segmentation; Convolutional neural network; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009706884,0.002140268,0.0009348557,0.002649489,0.001113399,0.001457331,0.003263165,0.002077904,0.007423473],"category_scores_gemma":[0.002599706,0.0006005166,0.001535432,0.002723597,0.0008695003,0.001188506,0.001960642,0.002276924,0.01066108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188597,"about_ca_system_score_gemma":0.001148895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02112622,"about_ca_topic_score_gemma":0.0578228,"domain_scores_codex":[0.9986247,0.000209834,0.00008475425,0.0004952334,0.0004144211,0.0001711223],"domain_scores_gemma":[0.9985256,0.0002415824,0.00008191242,0.0005954747,0.000416736,0.000138776],"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":[0.000456104,0.0009006686,0.007932558,0.001380911,0.0002648624,0.0009284174,0.0002981885,0.0217548,0.01008201,0.003517077,0.867427,0.08505745],"study_design_scores_gemma":[0.0003913294,0.0004659423,0.0387909,0.000417238,0.0001913734,0.002842491,0.001274129,0.1778989,0.03945946,0.008713664,0.7292798,0.0002746963],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0803921,0.001250287,0.03744549,0.001110974,0.0008525976,0.001066062,0.8208734,0.03151181,0.02549739],"genre_scores_gemma":[0.04239623,0.0001809298,0.03411566,0.000181199,0.00004437882,0.0003666748,0.9187487,0.0008835383,0.003082742],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02112622,"threshold_uncertainty_score":0.04200649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827188055106587,"score_gpt":0.2938429142005202,"score_spread":0.2655710336494543,"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."}}