{"id":"W2565316591","doi":"10.1109/crv.2016.54","title":"Light at the End of the Tunnel: High-Speed LiDAR-Based Train Localization in Challenging Underground Environments","year":2016,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Train; Lidar; Point cloud; Computer science; Window (computing); Point (geometry); Real-time computing; Sliding window protocol; Remote sensing; Computer vision; Artificial intelligence; Geology; Geography","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":[0.000390621,0.0005076114,0.0003496645,0.0009203325,0.0003473244,0.0006741006,0.001085804,0.000739077,0.0009874165],"category_scores_gemma":[0.0007765671,0.0002718623,0.0003946155,0.0007699989,0.0003056463,0.0009783966,0.001137769,0.0005051479,0.0009158807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002033269,"about_ca_system_score_gemma":0.0005657271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003430152,"about_ca_topic_score_gemma":0.007202948,"domain_scores_codex":[0.9996719,0.00006343498,0.000008471437,0.00007019698,0.0001293599,0.00005655179],"domain_scores_gemma":[0.9997464,0.00004014171,0.00003907333,0.00005270795,0.00009700764,0.00002468869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003111886,0.0003088187,0.01650025,0.0003477726,0.0001186648,0.0007138409,0.0007473812,0.1066516,0.1769779,0.002845985,0.004407788,0.6900687],"study_design_scores_gemma":[0.00005533029,0.000207232,0.01222995,0.00005035873,0.0000532589,0.000769058,0.0004737946,0.9359165,0.0402028,0.002259919,0.007710931,0.00007093379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1077026,0.0002834771,0.8867909,0.000190069,0.00008371388,0.0000836687,0.0002179975,0.002055511,0.00259212],"genre_scores_gemma":[0.6398726,0.0002720653,0.3568385,0.00009139686,0.00005236336,0.0000893933,0.0005839778,0.0001051094,0.002094622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003430152,"threshold_uncertainty_score":0.006820381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0095474222427956,"score_gpt":0.2025689469695656,"score_spread":0.19302152472677,"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."}}