{"id":"W3166952120","doi":"10.1007/978-3-030-36412-0_10","title":"A Randomized Approximation Algorithm for Metric Triangle Packing","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Triangle inequality; Approximation algorithm; Combinatorics; Disjoint sets; Randomized algorithm; Vertex (graph theory); Mathematics; Metric (unit); Graph; Discrete mathematics; Algorithm; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001519243,0.002498325,0.003683547,0.002143519,0.001839123,0.003654125,0.006864087,0.003300168,0.02365098],"category_scores_gemma":[0.0098224,0.001490696,0.001958749,0.007173244,0.00151463,0.006879766,0.00637608,0.004071031,0.007092196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003559572,"about_ca_system_score_gemma":0.003866675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005417398,"about_ca_topic_score_gemma":0.006568025,"domain_scores_codex":[0.9961002,0.0007579389,0.0002400162,0.0009021983,0.001434714,0.0005650666],"domain_scores_gemma":[0.9950464,0.001931632,0.0002572623,0.001838826,0.0005260006,0.0003999121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001898675,0.001147412,0.001067596,0.0005610784,0.0001533884,0.0001708569,0.0002429705,0.1377714,0.009286409,0.07337985,0.06120063,0.7131197],"study_design_scores_gemma":[0.0005397639,0.0003072782,0.0004496388,0.00005349269,0.00007224768,0.000267182,0.0001198593,0.8899399,0.003641584,0.09280992,0.01174706,0.00005203105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03386264,0.001284687,0.9303634,0.001477036,0.0007446743,0.0006162173,0.001226437,0.007702191,0.02272276],"genre_scores_gemma":[0.1424211,0.0005711097,0.8394514,0.0005189592,0.000359727,0.0007800648,0.003483745,0.0009269589,0.01148691],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02365098,"threshold_uncertainty_score":0.0791204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0277038657742993,"score_gpt":0.2624524088311046,"score_spread":0.2347485430568053,"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."}}