{"id":"W4255938318","doi":"10.1017/s0890060415000104","title":"AIE volume 29 issue 2 Cover and Front matter","year":2015,"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); Environmental science; Content (measure theory); 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":[0.001308907,0.0009478628,0.000936592,0.003714153,0.001631848,0.009466651,0.001114208,0.002722542,0.6970491],"category_scores_gemma":[0.003893639,0.0004119924,0.0007558935,0.002661662,0.0008520221,0.003741059,0.002314169,0.002552723,0.5997924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001395954,"about_ca_system_score_gemma":0.001504397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001059215,"about_ca_topic_score_gemma":0.002012519,"domain_scores_codex":[0.998583,0.0001085051,0.00005752849,0.0002067047,0.000913822,0.0001305187],"domain_scores_gemma":[0.9965397,0.0006087078,0.0001408878,0.0004230967,0.001556742,0.0007308768],"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":[0.00003388275,0.00006389778,0.0001640797,0.0001855633,0.000007386758,0.00004366594,0.00002279615,0.0000826075,0.000561005,0.003751967,0.9043688,0.09071449],"study_design_scores_gemma":[0.000003958574,0.000009672519,0.0003823342,0.00009941727,0.000002649075,0.00004951505,0.00002456244,0.0001023813,0.0001654055,0.001001989,0.9981545,0.00000370924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00088607,0.007997325,0.001589104,0.008469809,0.02919389,0.00009762216,0.001282743,0.0009422186,0.9495412],"genre_scores_gemma":[0.003246899,0.003187579,0.0007386681,0.001921675,0.006294674,0.00003314053,0.0009266268,0.0003900045,0.9832608],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3029509,"threshold_uncertainty_score":0.4321227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05705216295712534,"score_gpt":0.2247679417669329,"score_spread":0.1677157788098076,"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."}}