{"id":"W3108038062","doi":"10.1145/3488250","title":"Erasure-Resilient Sublinear-Time Graph Algorithms","year":2021,"lang":"en","type":"preprint","venue":"ACM Transactions on Computation Theory","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Israel Science Foundation; National Science Foundation","keywords":"Sublinear function; Degree (music); Property testing; Social connectedness; Erasure; Adjacency list; Mathematics; Algorithm; Discrete mathematics; Graph property; Computer science; Graph; Time complexity; Combinatorics; Theoretical computer science; Line graph","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":[0.0007994198,0.0005799989,0.0005691786,0.0007181307,0.0005494386,0.0008370986,0.002192266,0.0003319043,0.0002172288],"category_scores_gemma":[0.00004517657,0.0005738757,0.0004907791,0.001146173,0.0002211031,0.0004170148,0.0002043485,0.001360679,0.000113153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210493,"about_ca_system_score_gemma":0.0003895683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003579724,"about_ca_topic_score_gemma":0.00001519933,"domain_scores_codex":[0.9960048,0.0006810456,0.0005929656,0.001365221,0.0008854449,0.0004705092],"domain_scores_gemma":[0.9958814,0.001023628,0.0002747796,0.002089313,0.0005135829,0.0002172802],"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":[0.00007385234,0.0008541704,0.000003471789,0.0001022385,0.0004666891,0.00009364686,0.001347395,0.451654,0.00005874229,0.01046801,0.0001485413,0.5347293],"study_design_scores_gemma":[0.001355154,0.0004383714,0.0006004745,0.0005007617,0.0001735545,0.0001418798,0.0002150525,0.4965173,0.003312596,0.4947328,0.0004501322,0.00156192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003951311,0.0002995053,0.9909008,0.001288994,0.00182032,0.0005129525,0.00006101013,0.0008049017,0.0003602273],"genre_scores_gemma":[0.2506379,0.000115577,0.7469431,0.0007893598,0.0001892141,0.0001997732,0.0002166443,0.00008449242,0.0008239967],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5331674,"threshold_uncertainty_score":0.9996713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675515628261876,"score_gpt":0.2754805988845482,"score_spread":0.2487254426019295,"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."}}