{"id":"W2901591409","doi":"10.1109/wispnet.2018.8538681","title":"Estimating Pollution Contents in an Urban Area using Airborne Hyperspectral Thermal Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Support vector machine; Environmental science; Aerosol; Computer science; Artificial intelligence; Meteorology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013753,0.0003989569,0.0002143594,0.001727641,0.0001621826,0.0004574182,0.000187218,0.0003092218,0.0007782751],"category_scores_gemma":[0.0003132767,0.0001627291,0.0003141704,0.0009088421,0.00009000086,0.0004412008,0.000247325,0.0001830822,0.0003400079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001556166,"about_ca_system_score_gemma":0.0001425853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003254265,"about_ca_topic_score_gemma":0.005879204,"domain_scores_codex":[0.9998409,0.0000167826,0.000008251376,0.000037521,0.0000767,0.00001986407],"domain_scores_gemma":[0.9998764,0.00002727361,0.00003031782,0.00001107071,0.00004610275,0.000008882433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003871081,0.0004564321,0.2991426,0.0003765797,0.0002645168,0.0005874808,0.0004975509,0.07049189,0.3417445,0.0007597576,0.002198698,0.2830929],"study_design_scores_gemma":[0.0000147789,0.000217529,0.4424133,0.00003733409,0.0001368178,0.000369306,0.0007859986,0.4588417,0.09383801,0.0007067649,0.002583523,0.00005492159],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482954,0.0002025549,0.04679986,0.00004322137,0.00001908578,0.00004923371,0.0007121044,0.0006561966,0.003222312],"genre_scores_gemma":[0.9619802,0.0002218499,0.03533201,0.00001338837,0.00001642837,0.00003586863,0.001038392,0.00003346284,0.001328365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003254265,"threshold_uncertainty_score":0.006470621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07668034570090972,"score_gpt":0.2867936706596241,"score_spread":0.2101133249587144,"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."}}