{"id":"W4391766394","doi":"10.48550/arxiv.2402.06040","title":"Deep Learning for Data-Driven Districting-and-Routing","year":2024,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada","keywords":"Computer science; Routing (electronic design automation); Artificial intelligence; Machine learning; Data science; Computer network","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","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001803703,0.0006790013,0.0008326345,0.000562441,0.0004587625,0.00199055,0.003454507,0.000627649,0.000006776471],"category_scores_gemma":[0.0006750216,0.0006698414,0.000415019,0.0004965703,0.00007510703,0.0003361181,0.01074401,0.002291897,0.00001589481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003872038,"about_ca_system_score_gemma":0.0002883604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00172231,"about_ca_topic_score_gemma":0.001737742,"domain_scores_codex":[0.9953673,0.0001981819,0.0009646959,0.001961479,0.0004984318,0.001009856],"domain_scores_gemma":[0.9968946,0.0004522939,0.0006077882,0.001550259,0.0001965797,0.0002984636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002258506,0.0001196953,0.001444834,0.000592938,0.0006039301,0.0001330729,0.002409759,0.2246538,0.0001140498,0.6094204,0.002097409,0.1583875],"study_design_scores_gemma":[0.0001624657,0.00006783445,0.0001971372,0.0003344144,0.0001958494,0.00006396203,0.0001155652,0.9949283,0.00006206051,0.001490551,0.001710022,0.0006718311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008841422,0.003552109,0.9818677,0.002284249,0.0005905175,0.0008034799,0.00004103026,0.00179686,0.0002226134],"genre_scores_gemma":[0.7827401,0.000170012,0.21522,0.0003437378,0.0004440925,0.0002818755,0.0002570259,0.0000884213,0.0004547555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7738986,"threshold_uncertainty_score":0.9995753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896882794381542,"score_gpt":0.2576846397663595,"score_spread":0.2387158118225441,"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."}}