{"id":"W7112385251","doi":"","title":"Warehouse Efficiency Improvement through Inventory Management Techniques : Case Analysis","year":2025,"lang":"en","type":"other","venue":"Theseus (Ammattikorkeakoulujen)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Order (exchange); Warehouse; Inventory management; Investment (military); Economic order quantity; Order picking; Inventory control; Data warehouse; Legislature; Inventory investment","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003557065,0.0004659791,0.0002344416,0.002252202,0.002054918,0.002372933,0.001895605,0.0009030926,0.001561281],"category_scores_gemma":[0.005379506,0.0003061523,0.0005082599,0.003508406,0.001300223,0.0009820478,0.0009950252,0.0005599551,0.0001953421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00727343,"about_ca_system_score_gemma":0.003739873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05509373,"about_ca_topic_score_gemma":0.1177988,"domain_scores_codex":[0.9963272,0.001811096,0.0001303812,0.000197244,0.001054085,0.0004799014],"domain_scores_gemma":[0.9957914,0.002731355,0.0003588505,0.0004083281,0.0006072466,0.0001027612],"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":[0.001699125,0.006717931,0.1666059,0.00227997,0.0002883875,0.02140243,0.07732175,0.08781075,0.02213268,0.04492768,0.01473546,0.554078],"study_design_scores_gemma":[0.0005254509,0.007298652,0.170859,0.001582085,0.0006795808,0.01126147,0.2439718,0.2887296,0.09903225,0.009852926,0.165751,0.0004560693],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618753,0.000496223,0.01640958,0.0004157409,0.00001589217,0.0007953389,0.0002540754,0.00007359558,0.0196643],"genre_scores_gemma":[0.9500414,0.0006966381,0.04510806,0.00005297396,0.000005728602,0.0002221405,0.0001596787,0.00001640259,0.003696934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05509373,"threshold_uncertainty_score":0.1095461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757910370423074,"score_gpt":0.2829721785400626,"score_spread":0.2653930748358319,"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."}}