{"id":"W2950811824","doi":"10.48550/arxiv.1309.3010","title":"Signal recovery and frames that are robust to erasure","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mathematical Analysis and Transform Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Erasure; Signal recovery; Redundancy (engineering); Communication source; Algorithm; Computer science; SIGNAL (programming language); Matrix (chemical analysis); Mathematics; Theoretical computer science; Compressed sensing; Telecommunications","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005115379,0.0004230221,0.0008351611,0.0002414623,0.0001296857,0.0001374977,0.0004615732,0.0004513943,0.0010052],"category_scores_gemma":[0.0001543453,0.000385048,0.0003616766,0.0002759748,0.00008711463,0.0001550275,0.0005067211,0.0006448604,0.00009053535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007661146,"about_ca_system_score_gemma":0.0000449035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008049852,"about_ca_topic_score_gemma":0.00007875225,"domain_scores_codex":[0.9981838,0.0001944008,0.0002833854,0.0008375959,0.0001462927,0.0003545375],"domain_scores_gemma":[0.9979153,0.000641025,0.0002551564,0.0007126055,0.0001527468,0.0003232085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002627846,0.001088165,0.01345208,0.006278139,0.003068037,0.000538197,0.001673035,0.06295891,0.0001045411,0.8928879,0.01120233,0.0064859],"study_design_scores_gemma":[0.000292657,0.00005505765,0.0007137441,0.000513543,0.00067715,0.00000420023,0.0004271663,0.02898428,0.0001713733,0.9670689,0.0004485717,0.0006433242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5020975,0.00006365954,0.4926507,0.0002096208,0.00009640255,0.000467122,0.00003833678,0.0001069759,0.004269682],"genre_scores_gemma":[0.9576289,0.0002127079,0.03603425,0.0001417427,0.00008373504,0.00000433147,0.000008214082,0.0000471812,0.005838914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4566164,"threshold_uncertainty_score":0.999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214966479686931,"score_gpt":0.2377817458035843,"score_spread":0.02281526611665324,"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."}}