{"id":"W4388918862","doi":"10.1016/j.ifacol.2023.10.1278","title":"Drone-based Volume Estimation in Indoor Environments","year":2023,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"NCCR Catalysis; Innosuisse - Schweizerische Agentur für Innovationsförderung; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Volume (thermodynamics); Terrain; Computer science; Lidar; Process (computing); Estimation; Gaussian; Surface (topology); Computer vision; Artificial intelligence; Data mining; Remote sensing; Geography; Mathematics; Engineering; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102906,0.0001429351,0.0001428421,0.0001830437,0.00003596977,0.0000221942,0.00008176945,0.00009776594,0.00009315973],"category_scores_gemma":[0.0000412785,0.0001564189,0.0000377253,0.0003749449,0.00002081932,0.00008505939,0.00001132229,0.0001182084,0.0005253187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001108439,"about_ca_system_score_gemma":0.00001218092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002392339,"about_ca_topic_score_gemma":0.00003419648,"domain_scores_codex":[0.9991495,0.00001920381,0.0002217143,0.0001686271,0.000189333,0.0002516062],"domain_scores_gemma":[0.9997081,0.00003197942,0.00002318668,0.0001678688,0.000008070266,0.0000608628],"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":[0.000006983899,0.00002766945,0.003352756,0.00002853436,0.000007565386,0.00001595298,0.00008517691,0.9864337,0.005332776,0.00001918085,0.00003528476,0.004654427],"study_design_scores_gemma":[0.0005794408,0.00003113334,0.01845139,0.00002822901,0.000007297902,5.82651e-7,0.00003487584,0.9788887,0.001236727,0.00002575501,0.0005508446,0.0001650394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8712806,0.00006896529,0.1260758,0.0008695384,0.0003728355,0.0003187984,0.000051847,0.00054351,0.0004181224],"genre_scores_gemma":[0.9559625,0.00003471335,0.04245082,0.0001658678,0.00005659697,0.00001908598,0.0007828615,0.00005469478,0.0004728167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08468201,"threshold_uncertainty_score":0.6752084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008596476435787152,"score_gpt":0.2086126004866835,"score_spread":0.2000161240508963,"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."}}