{"id":"W4404129430","doi":"10.1016/j.autcon.2024.105850","title":"RGB-LiDAR sensor fusion for dust de-filtering in autonomous excavation applications","year":2024,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Korea Institute of Machinery and Materials","keywords":"Lidar; Excavation; RGB color model; Fusion; Sensor fusion; Remote sensing; Computer science; Computer vision; Artificial intelligence; Engineering; Geology; Geotechnical engineering","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":[0.0001269627,0.00009428751,0.0001014458,0.0002449452,0.00006800154,0.00006544945,0.00005187311,0.00007404205,0.0000259748],"category_scores_gemma":[0.00002502745,0.0001050538,0.00003338452,0.0003866591,0.00005498748,0.0002608494,0.00002250959,0.0001343753,0.00001621246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000198613,"about_ca_system_score_gemma":0.00004585647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004667753,"about_ca_topic_score_gemma":0.000008673594,"domain_scores_codex":[0.9992512,0.00001716354,0.0002761088,0.0002254529,0.0000614698,0.0001685728],"domain_scores_gemma":[0.9996371,0.0001242354,0.00005766369,0.0001280122,0.00003563333,0.00001734655],"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":[0.000006991871,0.00002783026,0.008907346,0.00005124241,0.000008192333,8.067835e-7,0.0001813616,0.003835714,0.01999308,0.2679186,0.00002072423,0.6990482],"study_design_scores_gemma":[0.0007534854,0.00004256518,0.01920588,0.0002982612,0.00002272132,0.00002005403,0.001474266,0.5907622,0.04055557,0.3403858,0.006060785,0.0004184149],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3821574,0.00001472092,0.6160138,0.000363139,0.0001495646,0.0004000561,0.000008547922,0.0003494167,0.0005432704],"genre_scores_gemma":[0.8594541,0.000002541802,0.1401251,0.000006738343,0.00009267258,0.0002386301,0.00004342684,0.00001261646,0.00002418211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6986297,"threshold_uncertainty_score":0.428397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01153120069397166,"score_gpt":0.2787275533048492,"score_spread":0.2671963526108775,"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."}}