{"id":"W2466004939","doi":"10.5194/isprs-archives-xli-b1-729-2016","title":"POLE-LIKE OBJECT EXTRACTION FROM MOBILE LIDAR DATA","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Lidar; Computer vision; Computer science; Artificial intelligence; Ranging; Photogrammetry; Plane (geometry); Remote sensing; Mathematics; Pattern recognition (psychology); Geography; Geometry","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":[0.0002463928,0.0007940045,0.0007865465,0.003954551,0.0003269986,0.0009839789,0.0006089769,0.0007834902,0.001109549],"category_scores_gemma":[0.0008108739,0.0003513379,0.0006674369,0.002464077,0.0002584215,0.0007093124,0.000771538,0.0004374812,0.001284536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526086,"about_ca_system_score_gemma":0.0004701889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00205107,"about_ca_topic_score_gemma":0.002753796,"domain_scores_codex":[0.9995041,0.000025739,0.00002708059,0.0001089269,0.0002545317,0.00007970579],"domain_scores_gemma":[0.9995849,0.00005022212,0.00005225458,0.00006224012,0.000219845,0.00003057424],"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":[0.0004282063,0.0001204241,0.01117834,0.0004474585,0.0001099648,0.001681982,0.0003064578,0.02308419,0.2485164,0.002026609,0.005392036,0.706708],"study_design_scores_gemma":[0.00003277367,0.0002543665,0.04173709,0.00006850301,0.00007557087,0.001869531,0.0006813025,0.7810611,0.1549255,0.00491868,0.01427955,0.00009617398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2413499,0.0007204941,0.7486745,0.0001301364,0.0000984896,0.0003578732,0.001631618,0.00365059,0.003386487],"genre_scores_gemma":[0.6507601,0.0003934005,0.3432054,0.00007604382,0.00005466205,0.0001192915,0.003629767,0.0001550992,0.001606309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003954551,"threshold_uncertainty_score":0.004078209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01944580758832579,"score_gpt":0.2675402957172996,"score_spread":0.2480944881289739,"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."}}