{"id":"W4246031020","doi":"10.32920/ryerson.14654985.v1","title":"Adaptive vector greedy splitting algorithm","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Orthonormal basis; Haar wavelet; Computer science; Greedy algorithm; Algorithm; Greedy randomized adaptive search procedure; Digital signal processing; MATLAB; Wavelet; Signal processing; SIGNAL (programming language); Basis (linear algebra); Wavelet transform; Discrete wavelet transform; Mathematics; Artificial intelligence","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.0002594745,0.0003732142,0.000444502,0.0001450667,0.0001061195,0.0004492223,0.002852056,0.0002725759,0.0001074116],"category_scores_gemma":[0.00008113492,0.000349514,0.000167634,0.0002711096,0.00004687906,0.0006550748,0.01393233,0.0009229034,0.00003515922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212125,"about_ca_system_score_gemma":0.0002420312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001409401,"about_ca_topic_score_gemma":0.000006468115,"domain_scores_codex":[0.9972438,0.0001325476,0.0004221749,0.001331989,0.000491247,0.0003782185],"domain_scores_gemma":[0.996669,0.0001520235,0.0002830825,0.002473144,0.0002764798,0.000146285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001756995,0.00008864613,0.00001085041,0.00004408948,0.00005240163,0.0001962053,0.0003249576,0.0001379056,0.0004733045,0.02455429,0.004417993,0.9696976],"study_design_scores_gemma":[0.0002480434,0.0001013042,0.0004281838,0.000929819,0.00001996661,0.00006579201,0.0001711136,0.8658012,0.06466786,0.05711225,0.008880293,0.001574157],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005441362,0.0004553253,0.9919351,0.0002755956,0.0009153853,0.0003192442,0.00002859431,0.001796623,0.004219685],"genre_scores_gemma":[0.006659971,0.00006543015,0.991919,0.0003324701,0.0001824475,0.000100638,0.0000575551,0.00002634275,0.0006561634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9681234,"threshold_uncertainty_score":0.9998957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953264159705101,"score_gpt":0.2874956060658719,"score_spread":0.2579629644688209,"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."}}