{"id":"W3047889481","doi":"10.1080/01431161.2020.1766146","title":"Unsupervised dimensionality reduction of hyperspectral images using representations of reflectance spectra","year":2020,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Hyperspectral imaging; Dimensionality reduction; Remote sensing; Reflectivity; Reduction (mathematics); Curse of dimensionality; Computer science; Artificial intelligence; Spectral line; Pattern recognition (psychology); Environmental science; Geology; Mathematics; Optics; Physics","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":[0.0002256608,0.0001350608,0.0002996168,0.0002173884,0.00003021293,0.0000275332,0.0001847863,0.00005893407,0.000009875928],"category_scores_gemma":[0.0003588699,0.0001433281,0.0001780277,0.0002926553,0.0001086902,0.0003227873,0.00002350762,0.0002501631,0.000001491761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900729,"about_ca_system_score_gemma":0.00008146711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003467241,"about_ca_topic_score_gemma":0.000001320343,"domain_scores_codex":[0.9982353,0.00007001757,0.0008263037,0.0001481151,0.0005931233,0.0001270725],"domain_scores_gemma":[0.9980575,0.00007726307,0.0005126157,0.000157654,0.001115005,0.000079905],"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":[0.00009439002,0.00001290476,0.00002347705,0.00002903255,0.0001541811,0.00003514472,0.0005911498,0.03255086,0.9521468,0.00004637087,0.0001073027,0.01420843],"study_design_scores_gemma":[0.0004148758,0.00003895453,0.0009420979,0.0002168354,0.00005372108,0.0005709059,0.0003596459,0.3235203,0.6730266,0.0007026925,0.00004664852,0.0001067297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7983031,0.0002295726,0.1984286,0.001328733,0.0008379752,0.00006917473,0.000007776574,0.00003535943,0.0007596726],"genre_scores_gemma":[0.7435727,0.00008618461,0.2559401,0.00001693133,0.0003538378,3.549801e-9,0.000003395965,0.00002200682,0.000004772972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2909695,"threshold_uncertainty_score":0.5844749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04145658259472484,"score_gpt":0.3066954782531256,"score_spread":0.2652388956584008,"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."}}