{"id":"W2132665604","doi":"10.5194/isprsarchives-xxxviii-5-w12-121-2011","title":"POINT-BASED VERSUS PLANE-BASED SELF-CALIBRATION OF STATIC TERRESTRIAL LASER SCANNERS","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Calibration; Laser scanning; Planar; Scanner; Laser; Plane (geometry); Remote sensing; Range (aeronautics); Point (geometry); Computer science; Feature (linguistics); Computer vision; Optics; Artificial intelligence; Physics; Geology; Engineering; Mathematics; Computer graphics (images); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005756774,0.0002307112,0.0002545645,0.0005262453,0.000149261,0.0004149274,0.0004890724,0.0002912706,0.00115759],"category_scores_gemma":[0.001855055,0.0001758512,0.0002826898,0.0009067601,0.0003041995,0.0006186199,0.0004705777,0.000290667,0.000405835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002712441,"about_ca_system_score_gemma":0.0003135118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001602035,"about_ca_topic_score_gemma":0.002102471,"domain_scores_codex":[0.9993783,0.0001199588,0.00001862026,0.0001064328,0.0003397631,0.00003684049],"domain_scores_gemma":[0.9992538,0.0001538679,0.0001039838,0.0001731269,0.0003009464,0.00001427811],"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":[0.0004304147,0.00007017722,0.02467324,0.0002532864,0.0001776873,0.0001191878,0.0004028196,0.3229445,0.2195672,0.004216269,0.001351913,0.4257934],"study_design_scores_gemma":[0.00002258295,0.0002282468,0.04404703,0.00003138353,0.00006075142,0.0004439477,0.0001861017,0.7640224,0.1849629,0.001611683,0.004320033,0.00006299852],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4543612,0.0004116843,0.5386067,0.00007133092,0.00003896285,0.00004589463,0.0001598933,0.001332882,0.004971438],"genre_scores_gemma":[0.9394268,0.0001209668,0.05941758,0.00001914204,0.000005240525,0.00001340448,0.0001528381,0.0000736897,0.0007702045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001602035,"threshold_uncertainty_score":0.003872514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02286956989269217,"score_gpt":0.243941717598148,"score_spread":0.2210721477054558,"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."}}