{"id":"W1999545598","doi":"10.1007/s001700170076","title":"Error Compensation for Three-Dimensional Line Laser Scanning Data","year":2001,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; National Research Council Canada","funders":"National Research Council Canada","keywords":"Compensation (psychology); Laser scanning; Line (geometry); Laser; Industrial and production engineering; Computer science; Engineering; Optics; Mechanical engineering; Mathematics; Physics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003336428,0.00009338986,0.0001169409,0.000104267,0.0001297653,0.00002295613,0.001235432,0.00006010767,0.00009513683],"category_scores_gemma":[0.0001160664,0.00006702831,0.00004037827,0.00007916346,0.0001505673,0.0002156312,0.0005052006,0.0002322753,0.00003014641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001118493,"about_ca_system_score_gemma":0.00001535898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000278712,"about_ca_topic_score_gemma":0.0001492018,"domain_scores_codex":[0.9990643,0.00001036201,0.0003000656,0.0001726901,0.0003076584,0.0001449142],"domain_scores_gemma":[0.9990768,0.0001326365,0.000326676,0.0003758846,0.00005733052,0.00003060657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003692076,0.0001159076,0.0006623181,0.000002229123,0.000128455,0.00003226789,0.00005602833,0.3175655,0.0370575,0.0004437538,0.004212637,0.6393542],"study_design_scores_gemma":[0.003236152,0.0003703608,0.01412264,0.0001445767,0.0001131692,0.002690643,0.0002862957,0.09326382,0.3095511,0.1306968,0.4450178,0.0005066632],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9417483,0.00003504558,0.03894465,0.01831059,0.0004157186,0.0001537881,0.00001189219,0.00004462267,0.0003354162],"genre_scores_gemma":[0.9663721,0.00001819287,0.03297158,0.0002669967,0.0001670513,0.000002012262,0.00002296591,0.00001260792,0.0001665166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6388476,"threshold_uncertainty_score":0.2733335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02912562044077322,"score_gpt":0.2977130963856354,"score_spread":0.2685874759448622,"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."}}