{"id":"W4251710382","doi":"10.1017/s0890060414000584","title":"AIE volume 28 issue 4 Cover and Front matter","year":2014,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Action (physics); Content (measure theory); Environmental science; Computer science; Engineering; Geography; Meteorology; Physics; Mathematics; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001270158,0.0009558763,0.0009292353,0.003785391,0.001617368,0.008849266,0.001153227,0.00274767,0.7258487],"category_scores_gemma":[0.003997475,0.0004175782,0.0007695349,0.002824695,0.0009005552,0.003720945,0.002260844,0.002546048,0.6372393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416321,"about_ca_system_score_gemma":0.001487899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061232,"about_ca_topic_score_gemma":0.001931946,"domain_scores_codex":[0.9985965,0.0001108687,0.00005497601,0.0001983071,0.0009056737,0.0001336995],"domain_scores_gemma":[0.9962673,0.0006556234,0.0001563368,0.0004603807,0.001721038,0.0007394318],"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":[0.00003581477,0.00006541076,0.0001736632,0.0001829629,0.000007498411,0.00004232909,0.00002248169,0.00008468197,0.0005951522,0.003676325,0.8980628,0.09705089],"study_design_scores_gemma":[0.000003989102,0.00001035368,0.0003770764,0.0000933632,0.000002840326,0.00004958378,0.00002691847,0.0001088398,0.0001834204,0.001019275,0.9981207,0.00000364766],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008918594,0.006687628,0.001549325,0.008056921,0.02400179,0.00008930896,0.00115633,0.0009643225,0.9566025],"genre_scores_gemma":[0.002875438,0.002633366,0.000678265,0.001547056,0.004861392,0.0000287443,0.0007786658,0.000354323,0.9862429],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2741513,"threshold_uncertainty_score":0.3910435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104460978701463,"score_gpt":0.2082374295347896,"score_spread":0.177192819747775,"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."}}