{"id":"W2095204883","doi":"10.1115/detc2006-99379","title":"A Multi-Agent System for Distributed, Internet Enabled Cutter/Workpiece Engagement Extractions","year":2006,"lang":"en","type":"article","venue":"","topic":"Injection Molding Process and Properties","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Distributed computing; Computer science; Computation; Scheduling (production processes); Overhead (engineering); Process (computing); The Internet; Multi-agent system; Engineering; Artificial intelligence; Algorithm; Operating system","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":[0.0001310887,0.0001311028,0.0001159684,0.00005619772,0.0001151959,0.00009130844,0.00008178131,0.00005388989,0.00009780846],"category_scores_gemma":[0.000007126428,0.0001112995,0.00006464878,0.00008440346,0.000008875909,0.000100668,0.0000164375,0.0001003873,0.00005292061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001552956,"about_ca_system_score_gemma":0.000006698016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002003588,"about_ca_topic_score_gemma":0.00006391776,"domain_scores_codex":[0.9993261,0.000013265,0.0002257186,0.0001419115,0.00007482707,0.0002182297],"domain_scores_gemma":[0.9997434,0.00002944063,0.00002397625,0.0001161261,0.00005507416,0.00003197438],"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":[0.0000735108,0.0002608116,0.001123299,0.002437925,0.0003169482,0.00001197916,0.0006762121,0.7799301,0.02301746,0.008212679,0.1783784,0.005560698],"study_design_scores_gemma":[0.0009070152,0.00005371253,0.0005045495,0.0001308913,0.00005609613,0.00002071329,0.0007422881,0.477293,0.05571047,0.0000281242,0.4641568,0.0003962842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01978211,0.0003055201,0.9739182,0.00005281875,0.001031271,0.0003566013,0.00004228702,0.0009700138,0.003541158],"genre_scores_gemma":[0.9855705,0.000008514075,0.007286839,0.00001772715,0.000162258,0.0002935042,0.00004885257,0.0000284214,0.006583357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9666314,"threshold_uncertainty_score":0.4538663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02708096450560985,"score_gpt":0.2301579385324287,"score_spread":0.2030769740268189,"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."}}