{"id":"W4416726276","doi":"10.1109/igarss55030.2025.11243732","title":"Depth Estimation of Channels Based on Total Station Point Clouds","year":2025,"lang":"","type":"article","venue":"","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Xihua University","keywords":"Channel (broadcasting); Point cloud; Position (finance); Overhead (engineering); Point (geometry); Orientation (vector space); Cloud computing","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":[0.0001751448,0.0006682348,0.0004347262,0.001881159,0.0002541041,0.0006814844,0.000624884,0.0003574731,0.0008155754],"category_scores_gemma":[0.0007521251,0.000354699,0.0005355117,0.001769429,0.0002670031,0.001008606,0.0009577076,0.000490609,0.0004124132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003420844,"about_ca_system_score_gemma":0.0008334185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008727788,"about_ca_topic_score_gemma":0.01162408,"domain_scores_codex":[0.9996387,0.00002408986,0.00001202936,0.00006748492,0.0001939656,0.00006368272],"domain_scores_gemma":[0.9996785,0.00004085839,0.0000537078,0.00005422389,0.0001480238,0.00002466614],"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":[0.0003204864,0.0001382177,0.02983148,0.0002679799,0.0001157823,0.0002958016,0.000351862,0.3021126,0.1680009,0.003289619,0.002922788,0.4923524],"study_design_scores_gemma":[0.0000267683,0.0000612493,0.02251331,0.00002292027,0.00003465937,0.0001705377,0.0001388836,0.941324,0.03247263,0.001489838,0.001701294,0.00004398813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1914267,0.0002041293,0.8041264,0.00004803425,0.00003559679,0.00007189721,0.0006374149,0.001762643,0.001687191],"genre_scores_gemma":[0.8226106,0.0002884867,0.1747102,0.00002716912,0.00002323557,0.0000692512,0.0009969957,0.00009586156,0.001178142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008727788,"threshold_uncertainty_score":0.01735395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005798835656286232,"score_gpt":0.2217280951763685,"score_spread":0.2159292595200823,"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."}}