{"id":"W4403616496","doi":"10.1007/s10479-024-06351-4","title":"Migratory beekeeping routing: a combinatorial optimization problem in apiculture","year":2024,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beekeeping; Theory of computation; Varroa; Computer science; Combinatorial optimization; Routing (electronic design automation); Mathematical optimization; Mathematics; Biology; Honey bee; Computer network; Ecology; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001471724,0.001552982,0.001654027,0.001270165,0.0009866924,0.004418619,0.002102282,0.002792436,0.004334481],"category_scores_gemma":[0.003809721,0.001005292,0.00138214,0.002932108,0.001764377,0.002578302,0.001176903,0.002302506,0.0004328903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205344,"about_ca_system_score_gemma":0.001615726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01575257,"about_ca_topic_score_gemma":0.01551873,"domain_scores_codex":[0.9992168,0.000384133,0.00003311637,0.0001843903,0.00009420275,0.00008728589],"domain_scores_gemma":[0.9979493,0.001602448,0.0001945145,0.00005987223,0.0001049815,0.00008894373],"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":[0.00006226914,0.0001180352,0.001290288,0.0003268048,0.0001295824,0.0001932754,0.00009991584,0.8792554,0.000850442,0.07432196,0.006130942,0.03722108],"study_design_scores_gemma":[0.00001559703,0.00003861536,0.0004070355,0.00005053212,0.00004122625,0.00006263852,0.0001304553,0.9543751,0.0002440545,0.04061814,0.003997826,0.00001871403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05579621,0.005616798,0.9129041,0.00232324,0.0003843516,0.0001414923,0.000566461,0.0001173421,0.02215001],"genre_scores_gemma":[0.6233419,0.01068444,0.3278271,0.0006060775,0.0006479773,0.0004305331,0.0008333575,0.0003829565,0.03524568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01575257,"threshold_uncertainty_score":0.0313217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1132346152020779,"score_gpt":0.417617561499046,"score_spread":0.304382946296968,"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."}}