{"id":"W4281481157","doi":"10.1016/j.jag.2022.102826","title":"Super-resolving and composing building dataset using a momentum spatial-channel attention residual feature aggregation network","year":2022,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; University of Waterloo; Central University of Finance and Economics","keywords":"Generalizability theory; Residual; Image resolution; Computer science; Artificial intelligence; Generalization; Channel (broadcasting); Feature (linguistics); Mean squared error; Pattern recognition (psychology); Machine learning; Algorithm; Data mining; Statistics; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007192672,0.0001205983,0.0001362118,0.000265984,0.0004179312,0.0004122772,0.0003204627,0.00003741965,0.000004272166],"category_scores_gemma":[0.00004033336,0.0001287517,0.0000247039,0.0002130425,0.00002853462,0.002798744,0.0003480993,0.0002554189,3.361494e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009427373,"about_ca_system_score_gemma":0.00005655895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001645943,"about_ca_topic_score_gemma":0.000003386765,"domain_scores_codex":[0.9985657,0.00003672619,0.0004792289,0.000147213,0.0006256007,0.0001455486],"domain_scores_gemma":[0.9987196,0.00005862103,0.0007689767,0.0001092709,0.000291614,0.00005187919],"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":[0.0008911209,0.0002066569,0.004245431,0.00020252,0.0003127871,0.00005171873,0.008469972,0.3284985,0.09480491,0.05032077,0.006068252,0.5059274],"study_design_scores_gemma":[0.0008365078,0.00008539884,0.005738407,0.000112374,0.00001655021,0.0002871215,0.0002585893,0.974382,0.001405412,0.01109366,0.005587756,0.0001961706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1282428,0.0001234756,0.8696752,0.001353696,0.0003585686,0.0001434561,0.00002567185,0.0000458949,0.00003115294],"genre_scores_gemma":[0.5835596,0.00006106084,0.4151675,0.0007178242,0.0002011592,0.000007017557,0.000268708,0.000008907214,0.000008131658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6458836,"threshold_uncertainty_score":0.5250342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794446700521962,"score_gpt":0.2625236860232593,"score_spread":0.2445792190180396,"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."}}