{"id":"W4292969385","doi":"10.1109/tgrs.2022.3201284","title":"Intracity Temperature Estimation by Physics Informed Neural Network Using Modeled Forcing Meteorology and Multispectral Satellite Imagery","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Remote sensing; Satellite; Multispectral image; Forcing (mathematics); Meteorology; Image resolution; Environmental science; Satellite imagery; Sea surface temperature; Radiative forcing; Computer science; Geography; Climatology; Geology; Artificial intelligence; Aerosol; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002997806,0.0006597058,0.0003733812,0.0004208199,0.0002312726,0.0004387098,0.0007662942,0.0005063516,0.0007410877],"category_scores_gemma":[0.0008381516,0.0003707004,0.000549847,0.0004050674,0.0003408866,0.0009509501,0.0005695288,0.0007484829,0.0001339578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007557094,"about_ca_system_score_gemma":0.0005660696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01717753,"about_ca_topic_score_gemma":0.01494823,"domain_scores_codex":[0.9998596,0.00002059033,0.000005816317,0.00006769672,0.00002017229,0.00002609807],"domain_scores_gemma":[0.9997733,0.00008619766,0.00004338426,0.00002436042,0.00005493959,0.00001776992],"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":[0.0000776822,0.00006496518,0.003528713,0.00002093531,0.00004289773,0.00003520053,0.00002863312,0.9409702,0.00227993,0.0008670851,0.0005680087,0.05151575],"study_design_scores_gemma":[9.504023e-7,0.000004672586,0.0002704036,5.500758e-7,0.000002143694,0.000001304782,0.000002057118,0.9992357,0.000145621,0.0003148887,0.00002041062,0.000001199179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3375438,0.000375797,0.6574103,0.0003203517,0.00008669286,0.0000523071,0.0002666582,0.001182627,0.002761452],"genre_scores_gemma":[0.9734411,0.00007996195,0.02516778,0.00005765478,0.00002381081,0.00003165627,0.0002580335,0.00002154558,0.0009185646],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01717753,"threshold_uncertainty_score":0.03415507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094951538216483,"score_gpt":0.22413332899947,"score_spread":0.2131838136173052,"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."}}