{"id":"W4398230519","doi":"10.48550/arxiv.2405.12876","title":"Approximating Traveling Salesman Problems Using a Bridge Lemma","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Lemma (botany); Bridge (graph theory); Mathematics; Combinatorics; Computer science; Biology; Botany; Anatomy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005954697,0.0004704701,0.0004696411,0.0003209189,0.0001366461,0.000170024,0.000495437,0.000437331,0.00002968692],"category_scores_gemma":[0.00005090226,0.0006208972,0.0002411033,0.000707552,0.00006541331,0.0001222071,0.0007679114,0.001309425,0.00005760538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004709483,"about_ca_system_score_gemma":0.0001089091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007901689,"about_ca_topic_score_gemma":0.00001299457,"domain_scores_codex":[0.9980298,0.0001307485,0.0004256649,0.0008275663,0.00009868905,0.0004874805],"domain_scores_gemma":[0.998904,0.0000893282,0.0001465694,0.0006166078,0.00009330973,0.0001502018],"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":[0.000003198818,0.00001310936,0.0002870648,0.001302878,0.0001541839,0.0001005857,0.0004509657,0.9903787,0.0005169559,0.006280876,0.00002306923,0.0004883776],"study_design_scores_gemma":[0.0002077257,0.000007305121,0.00006975517,0.0007618985,0.0002133985,0.00001516778,0.00007413377,0.9838437,0.0002320234,0.01392539,0.00004679825,0.0006026402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4068697,0.0001563829,0.5889769,0.000007341197,0.0005690616,0.000294932,0.00002283789,0.001098618,0.00200424],"genre_scores_gemma":[0.9456236,0.00007437447,0.05364338,0.00001110651,0.000199235,0.000001495963,0.00003156539,0.0001685314,0.0002466645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5387539,"threshold_uncertainty_score":0.9996243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1344195853920871,"score_gpt":0.2186557351095713,"score_spread":0.0842361497174842,"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."}}