{"id":"W4380538199","doi":"10.3390/rs15123083","title":"Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Plume; Convolutional neural network; Point cloud; Environmental science; Cloud computing; Key (lock); Computer science; Remote sensing; Meteorology; Artificial intelligence; Geology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003227656,0.0001176123,0.0001546283,0.00009197956,0.00007600193,0.00001778855,0.00004257087,0.0001198422,0.000004549648],"category_scores_gemma":[0.00004391333,0.000137463,0.000059865,0.0002586497,0.00001462549,0.00005218267,0.0000145274,0.0001481254,0.00007112561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001884153,"about_ca_system_score_gemma":0.000007936107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003352256,"about_ca_topic_score_gemma":0.0000638692,"domain_scores_codex":[0.9990186,0.00004644636,0.0002553668,0.0001502797,0.0001929034,0.0003364205],"domain_scores_gemma":[0.9997449,0.0000515997,0.00002831996,0.0001102297,0.000008587128,0.00005637959],"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":[0.00003308769,0.000002428271,0.000347715,0.00001737978,0.00003226941,0.000007590354,0.0001070694,0.9126219,0.004613609,0.000001704045,0.001635159,0.08058012],"study_design_scores_gemma":[0.0008440818,0.00001117761,0.001733893,0.00004780546,0.000006076663,0.000009029852,0.00002862095,0.9857861,0.0002029093,0.00004747249,0.01115081,0.0001320009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299729,0.0002432708,0.06050789,0.0001746532,0.006606735,0.0009268813,0.000009112842,0.0006579435,0.0009005543],"genre_scores_gemma":[0.9982173,0.000009435093,0.0008569369,0.00002438871,0.0006690609,1.628877e-7,0.00002269096,0.00003651839,0.0001634806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08044812,"threshold_uncertainty_score":0.560558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05423479803878462,"score_gpt":0.2291424153307274,"score_spread":0.1749076172919427,"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."}}