{"id":"W2928318746","doi":"","title":"Big Data Mining to Construct Truck Tours","year":2018,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Construct (python library); Truck; Big data; Computer science; Data mining; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006847511,0.0001909283,0.0002669859,0.0002915014,0.0007050134,0.0001313221,0.0049113,0.0001220377,0.0001268716],"category_scores_gemma":[0.0001330621,0.000236933,0.00006826314,0.001092898,0.0003315723,0.001430655,0.003348056,0.0002018793,0.0005395043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006379025,"about_ca_system_score_gemma":0.0001673562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001108003,"about_ca_topic_score_gemma":0.0003009752,"domain_scores_codex":[0.9979526,0.00008433631,0.0001928795,0.0009062317,0.0004359348,0.00042805],"domain_scores_gemma":[0.9966487,0.0001098606,0.000175115,0.002510384,0.0002172106,0.0003387211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001036949,0.0002269745,0.0581429,0.00003997445,0.0001910105,0.00007465267,0.008077634,0.00000674281,0.009989385,0.007824581,0.02959567,0.8857267],"study_design_scores_gemma":[0.003899581,0.001034968,0.5755475,0.0003931886,0.000209711,0.0002455132,0.005658823,0.01413845,0.009041803,0.002249587,0.3853289,0.002251949],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.901731,0.0000359933,0.08976389,0.002665672,0.0005819563,0.0002349866,0.0003496936,0.0001922418,0.004444531],"genre_scores_gemma":[0.6540461,0.000006105175,0.3436581,0.000345664,0.0002644253,5.107492e-7,0.0000863792,0.00001672556,0.001576041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8834748,"threshold_uncertainty_score":0.9661847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07045823812558576,"score_gpt":0.2641854376567361,"score_spread":0.1937271995311504,"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."}}