{"id":"W2883831062","doi":"10.1364/ao.57.006219","title":"Continuum removal for ground-based LWIR hyperspectral infrared imagery applying non-negative matrix factorization","year":2018,"lang":"en","type":"article","venue":"Applied Optics","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Hyperspectral imaging; Non-negative matrix factorization; Radiance; Matrix decomposition; Computer science; Remote sensing; Optics; Pattern recognition (psychology); Algorithm; Mathematics; Artificial intelligence; Physics; Eigenvalues and eigenvectors; Geology","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":[0.0001594757,0.0002578533,0.0002535351,0.0001348537,0.0001563633,0.0001491002,0.0001557806,0.0001702234,0.000008274477],"category_scores_gemma":[0.0001102236,0.0002915559,0.00007424673,0.0002978988,0.000141122,0.000158324,0.00001837681,0.0001662635,0.00007622087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230338,"about_ca_system_score_gemma":0.0000489254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003385299,"about_ca_topic_score_gemma":0.000003862093,"domain_scores_codex":[0.9987499,0.00001054978,0.000343226,0.0003065335,0.0002000628,0.0003898019],"domain_scores_gemma":[0.9989913,0.0002147943,0.000106535,0.0003787367,0.0002271323,0.00008150387],"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":[0.00008457919,0.000035233,0.00002055313,0.0001221803,0.00006317878,0.000004295906,0.0007098071,0.003500655,0.9867709,0.002078878,0.00182965,0.004780054],"study_design_scores_gemma":[0.001422221,0.00006743307,0.000901924,0.00003636421,0.00007432221,0.000007526062,0.0003978243,0.5758749,0.4151956,0.002624181,0.00284872,0.0005489372],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1116917,0.00001320716,0.8721852,0.00004232401,0.0006567205,0.001135216,0.00002266163,0.0005597934,0.01369321],"genre_scores_gemma":[0.7093294,0.000003939854,0.2895951,0.00004228801,0.0005502737,0.00005561388,0.00009142183,0.00009149714,0.0002404378],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5976377,"threshold_uncertainty_score":0.9999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507795107179944,"score_gpt":0.2455445984946564,"score_spread":0.230466647422857,"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."}}