{"id":"W2981939930","doi":"10.1145/3343031.3351047","title":"Band and Quality Selection for Efficient Transmission of Hyperspectral Images","year":2019,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hyperspectral imaging; Computer science; Bandwidth (computing); Artificial intelligence; Pixel; Remote sensing; Transmission (telecommunications); Full spectral imaging; Pattern recognition (psychology); Computer vision; Data mining; Telecommunications; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001145885,0.000054025,0.00008939666,0.00003577012,0.00001398586,0.00001023156,0.00001878319,0.00003431704,0.00001406816],"category_scores_gemma":[0.00001217784,0.0000479093,0.00002532404,0.0000552922,0.00001340971,0.00003625929,0.000001272296,0.00003240747,0.000002687539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000238737,"about_ca_system_score_gemma":0.000004832672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001168945,"about_ca_topic_score_gemma":0.000001185941,"domain_scores_codex":[0.9996399,0.000009690109,0.0001193831,0.00009508806,0.00005763624,0.00007833573],"domain_scores_gemma":[0.9998074,0.0000479226,0.00001604984,0.00006832169,0.00003845869,0.00002191722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001248479,0.0000108935,0.0002856939,0.0001207383,0.000005489898,9.933282e-9,0.00008938956,0.004687154,0.9872668,0.0001526505,0.0001150706,0.007253676],"study_design_scores_gemma":[0.0002506138,0.00003095486,0.00954397,0.00001080479,0.000005770638,0.000001242318,0.00004304472,0.2979151,0.6918656,0.00002080207,0.0002546651,0.00005745298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7967876,0.00006113132,0.1999356,0.00004788077,0.00005452561,0.0001785984,0.000001540529,0.0000825465,0.002850548],"genre_scores_gemma":[0.9811384,0.00001188219,0.01847775,0.000002607958,0.00001179445,8.935147e-7,0.000002423522,0.00001058119,0.0003436282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2954011,"threshold_uncertainty_score":0.1953684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615087635970561,"score_gpt":0.2542429653761034,"score_spread":0.2380920890163978,"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."}}