{"id":"W2026522103","doi":"10.3758/bf03211819","title":"A model of human performance on the traveling salesperson problem","year":2000,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Node (physics); Travelling salesman problem; Range (aeronautics); Heuristic; Variety (cybernetics); Mathematical optimization; Path (computing); Psychology; Algorithm; Mathematics; Computer science; Artificial intelligence; Engineering","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":[0.0006174166,0.0004990748,0.0005804606,0.0004452519,0.0003345572,0.001318675,0.001026882,0.001357785,0.007761521],"category_scores_gemma":[0.004017286,0.0002136662,0.0004359033,0.0006167176,0.0008048794,0.001387878,0.0004437294,0.000669585,0.0006470078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007835522,"about_ca_system_score_gemma":0.0009724383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01557704,"about_ca_topic_score_gemma":0.006845093,"domain_scores_codex":[0.9997198,0.000125784,0.000007321066,0.00006049634,0.00002280141,0.00006382828],"domain_scores_gemma":[0.9988569,0.0007649992,0.0000899462,0.00007982817,0.00008402702,0.0001242607],"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":[0.0003483511,0.0004014574,0.004964988,0.0001156134,0.0001307834,0.0003573434,0.0009074045,0.7774307,0.001602239,0.1786052,0.004719058,0.03041681],"study_design_scores_gemma":[0.00005103205,0.0001407003,0.002741491,0.00001490664,0.0000302141,0.00007336074,0.0002517951,0.9235224,0.0001604185,0.07213831,0.0008533088,0.00002195887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7766214,0.0007182569,0.1681365,0.002519968,0.00008801606,0.0000884964,0.0006286635,0.0002832332,0.05091542],"genre_scores_gemma":[0.9874617,0.0002235052,0.007421202,0.00006029269,0.00001945551,0.00005148871,0.0001177009,0.00001550674,0.004629159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01557704,"threshold_uncertainty_score":0.03097278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04561925188645836,"score_gpt":0.2545081547099788,"score_spread":0.2088889028235204,"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."}}