{"id":"W2952864736","doi":"10.48550/arxiv.1506.07952","title":"Tradeoffs Between Cost and Information for Rendezvous and Treasure Hunt","year":2015,"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":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec en Outaouais","keywords":"Treasure; Rendezvous; Advice (programming); Combinatorics; Omega; Upper and lower bounds; Binary logarithm; Traverse; Mathematics; Discrete mathematics; Computer science; Physics; Mathematical analysis; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003590693,0.001577066,0.001784002,0.001520015,0.001326522,0.003072916,0.004216849,0.00332776,0.007745855],"category_scores_gemma":[0.05032088,0.001100349,0.0009582822,0.001588549,0.002746736,0.01196977,0.003151126,0.003053963,0.0008677813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003158617,"about_ca_system_score_gemma":0.001743328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002875106,"about_ca_topic_score_gemma":0.00468587,"domain_scores_codex":[0.9964749,0.001061349,0.0001434384,0.0006088108,0.0008136151,0.0008978687],"domain_scores_gemma":[0.9185123,0.0715976,0.002207141,0.00460175,0.001228645,0.001852501],"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":[0.00302583,0.0004535,0.003505122,0.001027014,0.0001717643,0.0004017862,0.0006121282,0.7311722,0.019485,0.1475919,0.00724322,0.08531048],"study_design_scores_gemma":[0.0001358763,0.0003531786,0.001465218,0.0001141851,0.0001084775,0.0003776272,0.0002996683,0.8730238,0.007168002,0.1139998,0.002869169,0.00008504869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4808036,0.00693134,0.4395822,0.01172903,0.0003462035,0.0003538861,0.001502111,0.001787367,0.0569642],"genre_scores_gemma":[0.9056376,0.002505055,0.08493782,0.0004935066,0.0001823763,0.0002232548,0.0004880897,0.0005260825,0.005006196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007745855,"threshold_uncertainty_score":0.02591246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1335887771927533,"score_gpt":0.2103871983532926,"score_spread":0.07679842116053934,"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."}}