{"id":"W2103540378","doi":"10.1109/igarss.2007.4423107","title":"Investigation of nonlinearity in hyperspectral remotely sensed imagery — a nonlinear time series analysis approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hyperspectral imaging; Nonlinear system; Computer science; Time series; Remote sensing; Time domain; Statistic; Artificial intelligence; Pattern recognition (psychology); Series (stratigraphy); Independent component analysis; Gaussian process; Gaussian; Data mining; Mathematics; Statistics; Computer vision; Machine learning; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006567256,0.0001995046,0.0003874773,0.0006581938,0.00002815916,0.0000347819,0.0001113717,0.0001588632,0.00002303223],"category_scores_gemma":[0.0001530854,0.0002082591,0.0001340964,0.001704765,0.0001416874,0.000274006,0.00001911821,0.0002401515,0.00004230285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001457152,"about_ca_system_score_gemma":0.00003531673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001617431,"about_ca_topic_score_gemma":0.0002477046,"domain_scores_codex":[0.9985256,0.00005457551,0.0005773033,0.000283975,0.0002550258,0.0003035156],"domain_scores_gemma":[0.9991225,0.00009947694,0.00008911546,0.000457084,0.0001391088,0.00009270573],"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":[0.00004469982,0.00004322677,0.00546399,0.00008533079,0.0001549529,0.000005519632,0.0006370453,0.01826561,0.9740571,0.0000417779,0.0001383243,0.001062421],"study_design_scores_gemma":[0.0003053127,0.00001692238,0.1117666,0.00001717205,0.0001460298,0.00001928758,0.0002244393,0.7544244,0.1325095,0.00007858277,0.0001601181,0.0003316377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8470025,0.00002932754,0.144725,0.00008964898,0.00003843317,0.0001663562,0.000009472342,0.0002800966,0.007659174],"genre_scores_gemma":[0.4663844,0.000009188204,0.532892,0.00001756531,0.0000653473,4.860804e-7,0.0001757391,0.00002948621,0.000425807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8415476,"threshold_uncertainty_score":0.849256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261955037291134,"score_gpt":0.2333780776112847,"score_spread":0.2107585272383734,"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."}}