{"id":"W2103351706","doi":"10.1109/icassp.2012.6288775","title":"Approximating signals supported on graphs","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science","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":[0.00007362503,0.0000820615,0.00008156245,0.00005697956,0.0000234008,0.00001250048,0.00005186596,0.00004017886,0.000157445],"category_scores_gemma":[0.000007571814,0.00007060923,0.00003192036,0.00007647572,0.000008032172,0.00007573811,0.000009976658,0.00007650875,0.00006898215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008316504,"about_ca_system_score_gemma":0.000001161613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004078134,"about_ca_topic_score_gemma":2.864381e-7,"domain_scores_codex":[0.9995586,0.000008086087,0.0001015168,0.00005298773,0.00007413232,0.0002046799],"domain_scores_gemma":[0.9997643,0.00002661977,0.00001138324,0.0001363869,0.00001078008,0.00005052231],"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":[0.0000133197,0.0002226168,0.006458775,0.00005945471,0.0002015447,0.00001394815,0.001112877,0.005553887,0.6731395,0.06394726,0.1655585,0.08371833],"study_design_scores_gemma":[0.00008062936,0.00002470499,0.001190165,0.00003638768,0.000009052078,0.000007447376,0.0000555937,0.02373679,0.9683402,0.002245751,0.004045325,0.0002279288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6582224,0.0001682944,0.0320886,0.00002411242,0.0002523884,0.0001425106,0.000001231669,0.003328895,0.3057715],"genre_scores_gemma":[0.9926972,0.000005529582,0.006998037,0.000124671,0.00005859883,0.000005747437,0.000002625985,0.00001907873,0.00008852976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3344748,"threshold_uncertainty_score":0.2879361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411154975420832,"score_gpt":0.2379932250956133,"score_spread":0.213881675341405,"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."}}