{"id":"W4411659899","doi":"10.1016/j.dib.2025.111837","title":"Order picking dataset from a warehouse of a footwear manufacturing company","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Order (exchange); Warehouse; Computer science; Manufacturing engineering; Business; Data science; Engineering; Marketing; Finance","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":[0.0006737884,0.001283167,0.0008798732,0.003101759,0.000967934,0.001881251,0.002037437,0.001630797,0.008239705],"category_scores_gemma":[0.002663677,0.0003655937,0.0009475703,0.006290886,0.0003580695,0.001010414,0.001347618,0.001245429,0.01059157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557338,"about_ca_system_score_gemma":0.001866308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02212238,"about_ca_topic_score_gemma":0.04478509,"domain_scores_codex":[0.9986689,0.0001342121,0.0001720067,0.0003112685,0.0005230749,0.0001905388],"domain_scores_gemma":[0.9982668,0.0003473242,0.0001624602,0.0003973045,0.000609289,0.0002168123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005127726,0.000720816,0.02336111,0.001633198,0.0001602549,0.0009346405,0.0004106048,0.01327074,0.004224823,0.003315815,0.9047205,0.04673482],"study_design_scores_gemma":[0.0002690241,0.0002388199,0.06554858,0.0004304047,0.00007883275,0.0006328113,0.001632071,0.02621502,0.006913546,0.003940524,0.8939247,0.0001758025],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03481267,0.0005645963,0.002079416,0.0004182887,0.0001211891,0.00017002,0.954676,0.002132942,0.005024859],"genre_scores_gemma":[0.01544155,0.0001631528,0.003807703,0.0001061634,0.00001570075,0.000130633,0.9792117,0.00007127089,0.0010522],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02212238,"threshold_uncertainty_score":0.04398721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285658327735635,"score_gpt":0.2662290104307193,"score_spread":0.243372427153363,"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."}}