{"id":"W4407309047","doi":"10.48550/arxiv.2502.04632","title":"Tight Bounds for Noisy Computation of High-Influence Functions, Connectivity, and Threshold","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; Johns Hopkins University; University of California, San Diego; National Science Foundation","keywords":"Computation; Computer science; Statistical physics; Algorithm; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001114746,0.0002076551,0.0003263167,0.00009683696,0.0001045616,0.00002014128,0.0001106419,0.0001517666,0.000002115471],"category_scores_gemma":[0.00005602364,0.000226626,0.00006824603,0.0001014272,0.00004805894,0.0001074396,0.0001990646,0.0003284747,0.000002096063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004478482,"about_ca_system_score_gemma":0.00002783779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001300365,"about_ca_topic_score_gemma":0.00001050199,"domain_scores_codex":[0.9991496,0.00001454772,0.0002736319,0.0003137059,0.00007907023,0.0001694935],"domain_scores_gemma":[0.9993358,0.0002308928,0.0000923382,0.0002096987,0.00009216996,0.00003913055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000036063,0.00002480175,0.02981815,0.001794769,0.0001024,0.000002366218,0.0001238735,0.9504149,0.01336986,0.000658578,0.0002042941,0.003449952],"study_design_scores_gemma":[0.002470967,0.0003065096,0.5460706,0.002639005,0.0004185142,0.00001813614,0.0001094975,0.2466521,0.1733932,0.02408714,0.002098869,0.001735384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9179819,0.0005819201,0.0796959,0.00005837829,0.0009047427,0.0003404031,0.00004789643,0.0002104853,0.0001784233],"genre_scores_gemma":[0.9986641,0.00005784119,0.0008804321,0.00004395616,0.0001146753,0.00003848254,0.00004014142,0.00002001377,0.0001403229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7037628,"threshold_uncertainty_score":0.924154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02959045935018039,"score_gpt":0.273776750474174,"score_spread":0.2441862911239936,"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."}}