{"id":"W2027854745","doi":"10.1016/j.tcs.2010.10.026","title":"Reconstructing polygons from scanner data","year":2010,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; University of Waterloo","funders":"","keywords":"Scanner; Point in polygon; Point (geometry); Visibility; Range (aeronautics); Set (abstract data type); Polygon (computer graphics); Mathematics; Plane (geometry); Algorithm; Computational geometry; Computer science; Combinatorics; Geometry; Computer vision; Topology (electrical circuits); Polygon mesh; Artificial intelligence; Geography","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":[0.0004929063,0.001418126,0.001096155,0.001902398,0.0003114339,0.001437205,0.001211578,0.001487074,0.001783773],"category_scores_gemma":[0.003819175,0.001794731,0.001176768,0.002132847,0.0009803594,0.001471368,0.001550172,0.001779078,0.001474534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003361212,"about_ca_system_score_gemma":0.0009647985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003865831,"about_ca_topic_score_gemma":0.003919998,"domain_scores_codex":[0.9994053,0.00009153493,0.0000366223,0.0001385609,0.0002690002,0.00005908418],"domain_scores_gemma":[0.9985323,0.0006642865,0.0001304905,0.0004514831,0.0001666796,0.00005476104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004107704,0.0001115021,0.004183463,0.0004558904,0.0001172731,0.001232469,0.0004107412,0.501018,0.04949049,0.0147502,0.003319478,0.4244997],"study_design_scores_gemma":[0.00002065084,0.00004779895,0.0007766606,0.00002710916,0.00002391613,0.0004105651,0.0001246625,0.9653161,0.01868265,0.01178969,0.002754857,0.00002536787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03865306,0.0001603098,0.9580444,0.00009569128,0.00002988138,0.00007584104,0.0003692389,0.00163119,0.0009403831],"genre_scores_gemma":[0.318747,0.0005304864,0.6769508,0.00003876727,0.00002560413,0.00009057495,0.001517219,0.0004738154,0.00162577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003865831,"threshold_uncertainty_score":0.007686675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172743724347129,"score_gpt":0.2241116123313344,"score_spread":0.2123841750878631,"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."}}