{"id":"W4283800865","doi":"10.1609/aaai.v36i11.21595","title":"A Multimodal Fusion-Based LNG Detection for Monitoring Energy Facilities (Student Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"IntelliView Technologies (Canada); Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Liquefied natural gas; Fuse (electrical); Transformer; Infrared; Fossil fuel; Fusion; Computer science; Natural gas; Environmental science; Systems engineering; Engineering; Process engineering; Waste management; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003352497,0.0001473405,0.0001265508,0.00006562575,0.0006521911,0.00006538573,0.0004388487,0.00004512222,0.0009562477],"category_scores_gemma":[0.0001195331,0.0001290739,0.0001192463,0.0002744473,0.0001597327,0.000143138,0.0001923297,0.0001830377,0.00002476282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002410613,"about_ca_system_score_gemma":0.00002153112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005954194,"about_ca_topic_score_gemma":0.00009813897,"domain_scores_codex":[0.9985576,0.00001208257,0.0003430141,0.0003286535,0.0005279726,0.0002307322],"domain_scores_gemma":[0.9994531,0.00005591358,0.0002418038,0.0001105544,0.0000815495,0.0000571066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002492674,0.0002262534,0.001498681,0.00002661677,0.000008715573,1.561449e-7,0.001197413,0.007929838,0.731694,0.00955865,0.00004683972,0.2475636],"study_design_scores_gemma":[0.00005395882,0.0002733559,0.002797389,0.0000253032,0.000009414451,0.000001122022,0.003674392,0.02778315,0.9562017,0.007202227,0.001809031,0.0001689213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907888,0.000006201559,0.002370824,0.0004394646,0.0007534475,0.0003934501,0.00002007473,0.00006527018,0.005162503],"genre_scores_gemma":[0.9987513,0.00000538408,0.0002305387,0.00007160218,0.00005465222,0.000288997,9.578129e-7,0.00001196107,0.0005845629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2473946,"threshold_uncertainty_score":0.999957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04360313648313464,"score_gpt":0.2748511147392235,"score_spread":0.2312479782560889,"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."}}