{"id":"W2165175345","doi":"10.24908/pceea.v0i0.3975","title":"TOOL SELECTION-EMBEDDED OPTIMAL ASSEMBLY PLANNING","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Plan (archaeology); Task (project management); Computer science; Process (computing); Seven Management and Planning Tools; Systems engineering; Engineering; Operations management; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000234728,0.0001713839,0.0001490961,0.0002836998,0.0001495615,0.00008423953,0.0002485835,0.0001723771,0.00007623796],"category_scores_gemma":[0.0002669624,0.000178472,0.00006807085,0.0004390522,0.000007967794,0.0002571692,0.00001412713,0.0002343933,0.000009250984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001150235,"about_ca_system_score_gemma":0.0002521321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001319585,"about_ca_topic_score_gemma":0.0004030222,"domain_scores_codex":[0.9990089,0.000002867362,0.0002765401,0.000156408,0.0002314719,0.0003238001],"domain_scores_gemma":[0.9992668,0.00001811689,0.0001629605,0.00007327557,0.0003433518,0.0001354699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001125456,0.0001276507,0.1229339,0.001238806,0.0004579225,2.99319e-7,0.01251903,0.7914898,0.005126356,0.01178043,0.05136858,0.002945942],"study_design_scores_gemma":[0.0006002185,0.00005680116,0.4501081,0.0005327465,0.0002077313,0.00001749987,0.000857966,0.397881,0.1201695,0.000398221,0.02771835,0.001451814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688663,0.0001273747,0.0009480753,0.0002352145,0.002264958,0.0004433539,0.00001410074,0.0005344044,0.02656619],"genre_scores_gemma":[0.9928162,0.000006088076,0.00575602,0.0000605191,0.0001501765,0.00005502614,0.000005495041,0.00005056874,0.0010999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3936088,"threshold_uncertainty_score":0.7277876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008099279105180613,"score_gpt":0.1885560531319224,"score_spread":0.1804567740267418,"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."}}