{"id":"W4400992520","doi":"10.69554/ndue1531","title":"Collaborative mobile robots: Bringing greater productivity and flexibility to warehouse operations","year":2022,"lang":"en","type":"article","venue":"Journal of supply chain management, logistics and procurement.","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Warehouse; Flexibility (engineering); Productivity; Computer science; Robot; Manufacturing engineering; Business; Human–computer interaction; Engineering; Artificial intelligence; Marketing; Management; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000523996,0.0001564793,0.0001998865,0.0001951922,0.0002834395,0.0000913387,0.0001100993,0.00002080811,0.00003443661],"category_scores_gemma":[0.00005381609,0.0001520941,0.00002009142,0.0002100268,0.00004382541,0.0001119689,0.0001689902,0.0002035268,4.506912e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001277587,"about_ca_system_score_gemma":0.00001276676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005477453,"about_ca_topic_score_gemma":0.000008717203,"domain_scores_codex":[0.9990157,0.00004579392,0.0003166523,0.0002012895,0.0002260953,0.0001943944],"domain_scores_gemma":[0.9995451,0.00002567183,0.00007836039,0.0001439693,0.0001161785,0.0000907039],"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":[0.00003552914,0.00004429274,0.0004637664,0.0001259452,0.00007844652,0.00001945077,0.0009838952,0.993232,0.00009466249,0.0007797274,0.00077077,0.003371491],"study_design_scores_gemma":[0.007771634,0.005018101,0.01755742,0.0004159092,0.001170525,0.0003093187,0.02497896,0.7705961,0.006951974,0.01722414,0.1445056,0.003500312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0913895,0.0009566739,0.9044523,0.0005014919,0.0005263204,0.001216163,0.0001010192,0.0001027225,0.000753805],"genre_scores_gemma":[0.9692097,0.0003222621,0.02997496,0.00006247774,0.00007837923,0.00007677616,0.000008400551,0.00002271989,0.0002443389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8778202,"threshold_uncertainty_score":0.6202216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348705200307688,"score_gpt":0.2402779639263112,"score_spread":0.2267909119232343,"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."}}