{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003449209,0.0007332863,0.000342045,0.000674879,0.0001869381,0.0004177148,0.0006530909,0.0005651396,0.0009056506],"category_scores_gemma":[0.0007168186,0.0002387977,0.0003486559,0.0005919563,0.0001560039,0.0006406136,0.0005006734,0.0008161017,0.0003501626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007743676,"about_ca_system_score_gemma":0.0006756674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01598659,"about_ca_topic_score_gemma":0.02164137,"domain_scores_codex":[0.9998105,0.00002033158,0.000008468008,0.00005978202,0.00006103394,0.00003991889],"domain_scores_gemma":[0.9998296,0.00004587985,0.00002705363,0.00002013556,0.00006366459,0.00001361754],"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":[0.0005252034,0.0003421526,0.0130488,0.0001406524,0.00009162851,0.0002271445,0.00007554599,0.4344179,0.04342242,0.00153856,0.003479628,0.5026904],"study_design_scores_gemma":[0.000003017325,0.00001905272,0.001714359,0.000004204106,0.000007119244,0.00001263688,0.00000912488,0.992021,0.00559203,0.0003486235,0.0002633796,0.000005480954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4299424,0.001372518,0.5566753,0.0004004817,0.0001422568,0.0000717272,0.001265382,0.005410831,0.004719172],"genre_scores_gemma":[0.9484762,0.000240861,0.04801761,0.00007432585,0.00002114056,0.00002291655,0.001085712,0.00004068532,0.002020512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01598659,"threshold_uncertainty_score":0.0317871,"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."}}