{"id":"W2026760257","doi":"10.1016/s0030-4018(99)00759-2","title":"A correlation matrix representation using sliced orthogonal nonlinear generalized decomposition [Opt. Commun. 172 (1999) 181–192]","year":2000,"lang":"en","type":"article","venue":"Optics Communications","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correlation; Representation (politics); Diagonal; Nonlinear system; Binary number; Matrix (chemical analysis); Matrix representation; Gaussian; Pattern recognition (psychology); Robustness (evolution); Noise (video); Computer science; Mathematics; Algorithm; Artificial intelligence; Optics; Image (mathematics); Physics; Geometry; Arithmetic","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001439508,0.0002225544,0.0002470611,0.0001628492,0.0004348185,0.0001063228,0.0007971144,0.0001649958,0.0001046983],"category_scores_gemma":[0.00008234676,0.0002660112,0.00009145794,0.0005473632,0.0002207167,0.0004620609,0.0002173531,0.0005224287,0.0001291158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001657316,"about_ca_system_score_gemma":0.00002652505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003030471,"about_ca_topic_score_gemma":0.00004359946,"domain_scores_codex":[0.9986768,0.0001055221,0.0005249177,0.0001999273,0.0001961263,0.0002966458],"domain_scores_gemma":[0.997391,0.0003004906,0.00008893262,0.002013891,0.0001324367,0.00007323687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002379994,0.000156548,0.0004800654,0.0000336975,0.00007798963,0.000002561737,0.0002821728,0.9509316,0.0145362,0.01931651,0.00009566623,0.01406317],"study_design_scores_gemma":[0.0004517996,0.00001658265,0.0004141512,0.0001036532,0.00006559536,0.00002187712,0.00009881866,0.9942788,0.001055175,0.002402666,0.0008069155,0.0002839856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.583205,0.002242143,0.3877037,0.001390716,0.0002576562,0.001041684,0.00009448528,0.003367859,0.02069672],"genre_scores_gemma":[0.3998933,0.001701307,0.5976861,0.0000403425,0.00002932052,0.00005084462,0.0004630805,0.00005250032,0.00008326465],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2099824,"threshold_uncertainty_score":0.9999792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837853155548975,"score_gpt":0.3507657060117978,"score_spread":0.312387174456308,"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."}}