{"id":"W2984281342","doi":"10.1109/iccv.2019.00300","title":"DAGMapper: Learning to Map by Discovering Lane Topology","year":2019,"lang":"en","type":"article","venue":"","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Topology (electrical circuits); Computer science; Focus (optics); Inference; Directed acyclic graph; Network topology; A priori and a posteriori; Simple (philosophy); Precision and recall; ENCODE; Graph; Theoretical computer science; Algorithm; Artificial intelligence; Mathematics","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":[0.001084444,0.00235211,0.0012554,0.004332433,0.0007553091,0.001629037,0.00297997,0.001781846,0.005290715],"category_scores_gemma":[0.004141541,0.001015712,0.001625085,0.004047526,0.0006009521,0.002631439,0.002079603,0.001683841,0.004602635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008018119,"about_ca_system_score_gemma":0.001710711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01318635,"about_ca_topic_score_gemma":0.0240208,"domain_scores_codex":[0.9992762,0.0001278262,0.00003717138,0.0003262814,0.0001575414,0.00007495689],"domain_scores_gemma":[0.9989746,0.0004197785,0.00008201063,0.0002816846,0.0001847269,0.0000572194],"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":[0.0002442407,0.0002855848,0.005960722,0.0005346829,0.000359626,0.0003063311,0.0002126459,0.1891979,0.004578209,0.005867811,0.09125119,0.701201],"study_design_scores_gemma":[0.00006206987,0.00005145597,0.001117663,0.00003941527,0.0000616469,0.0001957353,0.0001410427,0.9390283,0.004386493,0.03421409,0.02066284,0.00003941054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01726574,0.0006483056,0.9272202,0.0004307388,0.0001696739,0.0002301462,0.007433503,0.04391,0.002691799],"genre_scores_gemma":[0.2033979,0.0006689677,0.7652117,0.0003891984,0.0001054654,0.0003675319,0.02321241,0.001467083,0.005179716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01318635,"threshold_uncertainty_score":0.02621925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002265042581719025,"score_gpt":0.1953818820821686,"score_spread":0.1931168395004496,"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."}}