{"id":"W2806791586","doi":"10.5194/isprs-annals-iv-2-137-2018","title":"AN EFFICIENT, HIERARCHICAL VIEWPOINT PLANNING STRATEGY FOR TERRESTRIAL LASER SCANNER NETWORKS","year":2018,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Workspace; Laser scanning; Scanner; Viewpoints; Table (database); Object (grammar); Plan (archaeology); Quality (philosophy); Visibility; Computer vision; Artificial intelligence; Mathematical optimization; Data mining; Mathematics; Laser","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001572285,0.000147802,0.0001947856,0.0001034182,0.0008319671,0.0003433437,0.0002672428,0.00008606907,0.00002207458],"category_scores_gemma":[0.0002007922,0.00008856027,0.00009421106,0.0004409384,0.0006996876,0.0003213079,0.00001988419,0.0001307272,0.000003577644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001804732,"about_ca_system_score_gemma":0.00005260329,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08010052,"about_ca_topic_score_gemma":0.02045441,"domain_scores_codex":[0.9985124,0.0001613311,0.0004023057,0.0001913433,0.0003525838,0.0003799795],"domain_scores_gemma":[0.9991308,0.000164763,0.000248487,0.0001695475,0.0001437871,0.000142551],"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":[0.0001131376,0.000004945216,0.001115651,0.000008283852,0.000007163148,1.769256e-7,0.0004865028,0.05417196,0.00002268254,0.000001718207,0.0002261866,0.9438416],"study_design_scores_gemma":[0.0001976753,0.0005004352,0.01527957,0.00006406356,0.000006892298,0.00000889197,0.0005242581,0.9806753,0.0009664204,0.000309793,0.001326198,0.0001405098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.727493,0.00009089086,0.2688456,0.0005923277,0.00102848,0.0003529145,0.00005668526,0.00004353574,0.001496579],"genre_scores_gemma":[0.9981902,0.00001215889,0.0007907193,0.0006963903,0.0002618792,6.821347e-8,0.00003290624,0.000002180633,0.00001345824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9437011,"threshold_uncertainty_score":0.9974198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07968612938027628,"score_gpt":0.3264698147726334,"score_spread":0.2467836853923571,"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."}}