{"id":"W1969798922","doi":"10.1007/978-3-540-69903-3_35","title":"Computing the Greedy Spanner in Near-Quadratic Time","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Spanner; Greedy algorithm; Computer science; Dimension (graph theory); Logarithm; Quadratic equation; Time complexity; Euclidean space; Upper and lower bounds; Metric (unit); Combinatorics; Algorithm; Mathematical optimization; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001174658,0.0004833197,0.0004746627,0.000706838,0.0005744818,0.0006825578,0.002862545,0.0002337312,0.00002676332],"category_scores_gemma":[0.0001165985,0.0003811132,0.0001306769,0.001402585,0.0006942434,0.0005563475,0.001163108,0.000906336,0.000191656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002714762,"about_ca_system_score_gemma":0.0008542317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003457513,"about_ca_topic_score_gemma":0.0000763054,"domain_scores_codex":[0.9961978,0.00008822454,0.0006622843,0.001291959,0.001119569,0.0006401767],"domain_scores_gemma":[0.9974091,0.0008825856,0.0002875937,0.001073892,0.000225369,0.000121477],"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":[0.000003584367,0.00003142455,0.0000484508,0.0000139166,0.000007431908,0.000113362,0.0016175,0.5834046,0.00006533977,0.003887232,0.0001378862,0.4106693],"study_design_scores_gemma":[0.0002279863,0.00008849673,0.0007610066,0.0001744591,0.000003574566,0.0001849764,1.19782e-7,0.970917,0.0001672784,0.02461321,0.002399794,0.0004621348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001197303,0.0006130483,0.9922057,0.001745214,0.00128145,0.0004516417,0.000001900004,0.0001110178,0.002392706],"genre_scores_gemma":[0.5229273,0.00008106916,0.4687734,0.004863751,0.001452998,0.00001358623,0.00002516118,0.00006573001,0.001797021],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5234324,"threshold_uncertainty_score":0.9998641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153657797872961,"score_gpt":0.2334040478138106,"score_spread":0.2180382680265144,"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."}}