{"id":"W4388964502","doi":"10.48550/arxiv.2311.12976","title":"Fast Deterministic Rendezvous in Labeled Lines","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec en Outaouais","keywords":"Rendezvous; Upper and lower bounds; Node (physics); Combinatorics; Matching (statistics); Path (computing); Binary logarithm; Integer (computer science); Mathematics; Time complexity; Discrete mathematics; Line (geometry); Position (finance); Computer science; Physics; Computer network; Geometry; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002828364,0.000227254,0.0002793407,0.0005014767,0.00008614062,0.0001503023,0.001670124,0.0002246609,0.00002293017],"category_scores_gemma":[0.00009028105,0.0002676889,0.0001018726,0.001027399,0.00006923667,0.0002190004,0.002125492,0.0005224501,0.0002792654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384675,"about_ca_system_score_gemma":0.0002357321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001688853,"about_ca_topic_score_gemma":0.0003264968,"domain_scores_codex":[0.998202,0.0001605197,0.0002254291,0.0009337285,0.0001024691,0.0003757991],"domain_scores_gemma":[0.9985312,0.0001340896,0.0001383147,0.0009218218,0.0001300207,0.0001446096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001313732,0.00009642596,0.003397148,0.0001006628,0.00002935429,0.001203192,0.0004148851,0.9356002,0.00001714474,0.05758521,0.0003907821,0.001151868],"study_design_scores_gemma":[0.000424016,0.00003634128,0.001424991,0.00009883365,0.000008876778,0.000002409315,0.00003388017,0.9754741,0.00001569767,0.02198563,0.0001921979,0.0003030872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07398026,0.00002817609,0.9203907,0.0004828869,0.001042927,0.0005159288,0.00002076434,0.0008333198,0.002705034],"genre_scores_gemma":[0.9858471,0.0002354541,0.003361535,0.00008000377,0.00004383811,0.000002237815,0.00002121588,0.00002192903,0.01038673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9170291,"threshold_uncertainty_score":0.9999775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1485900232893051,"score_gpt":0.22110459024088,"score_spread":0.07251456695157488,"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."}}