{"id":"W4417279416","doi":"10.1016/j.rsase.2025.101823","title":"MixerCA: An efficient and accurate model for high-performance hyperspectral image classification","year":2025,"lang":"en","type":"article","venue":"Remote Sensing Applications Society and Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Benchmark (surveying); Hyperspectral imaging; Pattern recognition (psychology); Process (computing); Channel (broadcasting); Segmentation; Feature (linguistics); Convolutional neural network; Contextual image classification; Convolution (computer science)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018364,0.0001856985,0.0001596254,0.00004162603,0.0003412589,0.00007654377,0.00005864116,0.0001218012,4.731831e-7],"category_scores_gemma":[0.000004486725,0.0002058941,0.00004373027,0.0001094465,0.0001854674,0.0001034182,0.00002085446,0.0001355807,0.000004923592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002282346,"about_ca_system_score_gemma":0.00001760505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007351468,"about_ca_topic_score_gemma":0.000001024188,"domain_scores_codex":[0.999023,0.00001362355,0.0002421446,0.0003938878,0.00009839163,0.0002289567],"domain_scores_gemma":[0.999402,0.00004903627,0.00005123018,0.0004005714,0.00002640383,0.00007071604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001102046,0.00004263255,0.000006021041,0.0002260509,0.00005394825,1.003336e-7,0.0008911362,0.2718762,0.3962503,0.001014919,0.0002412801,0.3293864],"study_design_scores_gemma":[0.0002773325,0.00001083444,0.002183562,0.00002528569,0.0000598522,0.000003910477,0.0002701646,0.9883166,0.007234101,0.000691029,0.0007371517,0.0001901612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3418398,0.0001360733,0.6567358,0.0005262835,0.00002464038,0.0004460275,0.000008067482,0.0001344989,0.000148827],"genre_scores_gemma":[0.688149,0.001401105,0.310176,0.00005581462,0.00003153582,0.000004597734,0.0000439182,0.00002511491,0.0001129179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7164404,"threshold_uncertainty_score":0.8396118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528278661306166,"score_gpt":0.2290380914331533,"score_spread":0.2137553048200916,"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."}}