{"id":"W4210987640","doi":"10.1109/tgrs.2022.3151004","title":"BCUN: Bayesian Fully Convolutional Neural Network for Hyperspectral Spectral Unmixing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Context (archaeology); Abundance estimation; Abundance (ecology)","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":[0.0004561404,0.0009366888,0.0004546632,0.0004056422,0.0003143987,0.0005151262,0.001428863,0.0009176325,0.002603392],"category_scores_gemma":[0.001011756,0.000411623,0.0003979048,0.000557622,0.0003842397,0.0008290886,0.0009774328,0.00132525,0.000888783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001223305,"about_ca_system_score_gemma":0.001351905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02696865,"about_ca_topic_score_gemma":0.03886161,"domain_scores_codex":[0.9997533,0.00003269352,0.000009785269,0.00006054153,0.00009913564,0.00004448588],"domain_scores_gemma":[0.9998176,0.00004424021,0.00002863424,0.00002337464,0.00006897625,0.00001719863],"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":[0.0002188201,0.0001312153,0.00103908,0.0001447456,0.0001334359,0.0001089822,0.00005992969,0.5205075,0.01601969,0.01955719,0.0152977,0.4267817],"study_design_scores_gemma":[0.000002656666,0.000008162794,0.0001357886,0.000005937783,0.000004909406,0.00001167813,0.000002243364,0.9947849,0.001688013,0.001953227,0.001396858,0.000005666737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01310865,0.001220106,0.9763951,0.0004222049,0.0001172843,0.00004826287,0.0004729974,0.003423137,0.004792151],"genre_scores_gemma":[0.4990879,0.002054936,0.4662669,0.0009001976,0.0001428748,0.0002600217,0.00345389,0.0005178239,0.02731551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02696865,"threshold_uncertainty_score":0.05362338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502962300378783,"score_gpt":0.2233599306975233,"score_spread":0.2083303076937355,"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."}}