{"id":"W2949499079","doi":"10.48550/arxiv.1709.06214","title":"Deterministic rendezvous with detection using beeps","year":2017,"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":"Rendezvous; Node (physics); Computer science; Mobile agent; Computer network; Real-time computing; Distributed computing; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001861227,0.0002368971,0.0002330168,0.0002440991,0.0004705237,0.000394515,0.001544609,0.000204403,0.00001265742],"category_scores_gemma":[0.00003453857,0.0002503393,0.00009049977,0.0002314264,0.0001362141,0.0004678169,0.001245661,0.0004620296,0.00003053481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001970227,"about_ca_system_score_gemma":0.000289087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001726692,"about_ca_topic_score_gemma":0.0001314575,"domain_scores_codex":[0.9984616,0.0001104649,0.0001300626,0.000871347,0.00011412,0.0003124018],"domain_scores_gemma":[0.9978804,0.00003921699,0.0003075704,0.001401,0.0002037892,0.0001680228],"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":[0.00003851044,0.00006237863,0.0007449342,0.00008431292,0.00007402225,0.0006434514,0.0002245891,0.980763,0.00007239907,0.01328822,0.00001300675,0.003991135],"study_design_scores_gemma":[0.0003530649,0.0000848747,0.0003324246,0.000093205,0.00003990515,0.00002517147,0.00001395893,0.9914204,0.0001191562,0.007010679,0.0001753032,0.0003318443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07298944,0.00001178596,0.9236306,0.00002993598,0.0003422587,0.0002417974,0.000004280898,0.0002057282,0.00254416],"genre_scores_gemma":[0.9897034,0.00004634045,0.00886126,0.00002887113,0.00004677263,7.154454e-7,0.000004177284,0.00001735165,0.001291093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.916714,"threshold_uncertainty_score":0.9999949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1397833646239472,"score_gpt":0.214835737831567,"score_spread":0.07505237320761982,"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."}}