{"id":"W2084989433","doi":"10.1109/lgrs.2014.2324631","title":"FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding","year":2014,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University College of the North","funders":"","keywords":"Hyperspectral imaging; Computer science; Visualization; Embedding; Pixel; Artificial intelligence; Dimensionality reduction; Computer vision; Nonlinear system; Data visualization; Metric (unit); Pattern recognition (psychology)","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":[0.0003812301,0.0001911623,0.000229132,0.0002279822,0.0001675784,0.00009917799,0.0000914835,0.00007085838,5.808853e-7],"category_scores_gemma":[0.0001491641,0.0001928091,0.00004933023,0.0004035451,0.0003457599,0.0003648427,0.00001775469,0.0001397395,0.000002907864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009372475,"about_ca_system_score_gemma":0.00001964074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008618097,"about_ca_topic_score_gemma":0.000004087692,"domain_scores_codex":[0.9986717,0.00006067366,0.0002933956,0.0003330974,0.000281087,0.0003600511],"domain_scores_gemma":[0.9993871,0.00007990866,0.0001096764,0.000263765,0.00007738572,0.00008218226],"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":[0.000003071498,0.000003249007,0.000009381806,0.00005001859,0.000005575939,0.000003015794,0.0003687034,0.03628938,0.9361092,0.000006466062,0.00002797413,0.02712391],"study_design_scores_gemma":[0.0001337788,0.00001300627,0.0005808175,0.0001500563,0.0000232239,0.00008507873,0.00006789994,0.9373732,0.0612816,0.00006781212,0.0000150949,0.0002084983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4908932,0.0000124907,0.5084009,0.00009500703,0.0003863719,0.0000735478,5.106431e-7,0.00009387154,0.00004414406],"genre_scores_gemma":[0.9036346,0.000006594622,0.096008,0.000158717,0.0001480693,1.872989e-8,0.000001824616,0.0000313355,0.00001080367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9010838,"threshold_uncertainty_score":0.7862526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158364527475842,"score_gpt":0.2498730583973773,"score_spread":0.2340366056497931,"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."}}