{"id":"W2799364841","doi":"10.1007/978-3-030-01325-7_14","title":"Symmetric Rendezvous with Advice: How to Rendezvous in a Disk","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ryerson University","keywords":"Rendezvous; Conjecture; Computer science; Energy (signal processing); Simple (philosophy); Point (geometry); Algorithm; Upper and lower bounds; Advice (programming); Line (geometry); Robot; Mathematics; Combinatorics; Geometry; Physics; Artificial intelligence; Mathematical analysis; Statistics","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","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001863675,0.0005698346,0.0006138216,0.002986586,0.0002245861,0.001929515,0.006009283,0.000303541,0.00000836842],"category_scores_gemma":[0.0005207002,0.0004693567,0.00008057721,0.01059537,0.0004764157,0.0007349796,0.005692386,0.001292718,0.00003012728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005786353,"about_ca_system_score_gemma":0.00124123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002729545,"about_ca_topic_score_gemma":0.001079907,"domain_scores_codex":[0.9937599,0.0002416846,0.0005022143,0.002602984,0.001628132,0.001265129],"domain_scores_gemma":[0.996069,0.0003868441,0.0002525305,0.002335857,0.0005255191,0.0004302971],"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":[0.00002077278,0.0001641451,0.002191498,0.00008661515,0.000009689593,0.0001521117,0.003859954,0.6948307,0.0000375591,0.0005109964,0.000183987,0.297952],"study_design_scores_gemma":[0.0004722508,0.000479164,0.003343228,0.0004706568,0.000003168964,0.00005064751,0.000001366234,0.9848246,0.000525103,0.008861081,0.0002305377,0.0007382169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00443867,0.0001411621,0.9853184,0.007283581,0.001401674,0.0009516849,0.000007708576,0.000296549,0.0001605522],"genre_scores_gemma":[0.4764453,0.00002043581,0.5221053,0.001172051,0.0001655923,0.00005697625,0.000003389627,0.00001871157,0.00001232441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4720066,"threshold_uncertainty_score":0.9997758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755097974014902,"score_gpt":0.2719166666561552,"score_spread":0.2543656869160061,"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."}}