{"id":"W2079589066","doi":"10.1109/m2rsm.2011.5697364","title":"Detection of Road Poles from Mobile Terrestrial Laser Scanner Point Cloud","year":2011,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Laser scanning; Segmentation; Computer vision; Computer science; Feature (linguistics); Artificial intelligence; Position (finance); Scanner; Pipeline (software); Lidar; Remote sensing; Geography; Laser; Optics; Physics","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":[0.0001291644,0.0003595277,0.000432263,0.001780638,0.0002964482,0.0004372233,0.0003906683,0.0004925677,0.000893848],"category_scores_gemma":[0.0005365187,0.0003554918,0.000361139,0.0009040625,0.0001997219,0.0005455699,0.0005443383,0.0003371628,0.0007567591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002167078,"about_ca_system_score_gemma":0.0004017322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003171326,"about_ca_topic_score_gemma":0.005935566,"domain_scores_codex":[0.9997779,0.00001604336,0.000005802402,0.00003435622,0.0001290747,0.00003679162],"domain_scores_gemma":[0.9997584,0.00004316794,0.00004163294,0.00003005772,0.0001049681,0.00002179128],"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":[0.0004942555,0.0001325224,0.030124,0.0002364316,0.00008364721,0.001216892,0.0003655797,0.07123663,0.431281,0.002444343,0.003781802,0.458603],"study_design_scores_gemma":[0.0000399189,0.0002082658,0.07780946,0.00004617387,0.00003467398,0.001042207,0.0004336159,0.7995878,0.1123983,0.002997589,0.00534454,0.00005745784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3799758,0.0002403473,0.6138951,0.00008035622,0.00002695658,0.0001293032,0.0005890239,0.002036649,0.003026405],"genre_scores_gemma":[0.8387294,0.0002026966,0.1589948,0.00001713368,0.00001460802,0.00006469464,0.001004852,0.00005794652,0.0009138607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003171326,"threshold_uncertainty_score":0.006305754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320874953796253,"score_gpt":0.2115960128625633,"score_spread":0.1983872633246007,"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."}}