{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002075492,0.0002645707,0.0002849801,0.0003648938,0.00004162552,0.00004427424,0.00009803345,0.0001699374,0.00008373757],"category_scores_gemma":[0.0001179179,0.0002926985,0.0001118464,0.0004944868,0.00002301753,0.0001158213,0.00001715883,0.0001963665,0.00002403223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001850793,"about_ca_system_score_gemma":0.00008141845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005164911,"about_ca_topic_score_gemma":0.000005791817,"domain_scores_codex":[0.9987635,0.00002275536,0.0004948623,0.0002263743,0.0002201841,0.0002723419],"domain_scores_gemma":[0.9993185,0.0001461165,0.00005619114,0.000337557,0.00007435716,0.00006723801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003505363,0.00007205889,0.00002660269,0.0005347045,0.00004694501,0.000001205478,0.0001422837,0.9643712,0.0004913392,0.01199115,0.0002692089,0.02201822],"study_design_scores_gemma":[0.0006534436,0.0001620259,0.001772181,0.0003883123,0.000045388,6.202048e-7,0.000053085,0.9890093,0.007209348,0.0004458904,0.00003345998,0.0002270091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07042097,0.00007313352,0.9082915,0.0001614337,0.00204303,0.000250568,0.00003574921,0.000237432,0.0184862],"genre_scores_gemma":[0.9829946,0.00001515942,0.01573108,0.00004079395,0.0000421555,0.00001442356,0.00007613174,0.00003416808,0.001051503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9125736,"threshold_uncertainty_score":0.9999525,"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."}}