{"id":"W4207037158","doi":"10.3390/rs14030530","title":"DA-IMRN: Dual-Attention-Guided Interactive Multi-Scale Residual Network for Hyperspectral Image Classification","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Residual; Pattern recognition (psychology); Feature (linguistics); Benchmark (surveying); Feature extraction; Scale (ratio); Spatial analysis; Feature learning; Pixel; Remote sensing; Geology; Algorithm; Cartography; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005159223,0.0002955076,0.0003079968,0.0001882266,0.0005700958,0.0001727497,0.0001362896,0.00009692289,0.00001667513],"category_scores_gemma":[0.0001524949,0.0003679714,0.0001719348,0.0004420589,0.00008303916,0.0002835413,0.00007261927,0.0004911991,0.00003775112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008187491,"about_ca_system_score_gemma":0.00005127064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004020737,"about_ca_topic_score_gemma":0.00002011919,"domain_scores_codex":[0.9978665,0.0001792654,0.0005318325,0.0005282455,0.0003298665,0.0005643158],"domain_scores_gemma":[0.9987222,0.0002172679,0.0001815355,0.0005599921,0.0002176322,0.0001013561],"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":[0.00006420019,0.00003208742,0.00001190355,0.00005373227,0.00009113718,0.00002552062,0.0008889543,0.05162823,0.8892647,0.00004368381,0.01236602,0.04552982],"study_design_scores_gemma":[0.0007167978,0.00003848982,0.001757901,0.00005282334,0.00006858972,0.0002017577,0.001243031,0.9830704,0.008608391,0.0002622373,0.003584902,0.0003946988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2748007,0.0001362023,0.7168469,0.001209813,0.002148525,0.0009970699,0.00004004926,0.0009914313,0.002829258],"genre_scores_gemma":[0.5089995,0.0000173375,0.4890057,0.00009083415,0.0007204351,6.704179e-7,0.0002722832,0.0001589179,0.0007343643],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9314421,"threshold_uncertainty_score":0.9998772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04051239353254007,"score_gpt":0.2863381116428912,"score_spread":0.2458257181103512,"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."}}