{"id":"W7073670190","doi":"","title":"Generative programming and component engineering third International Conference, GPCE 2004, Vancouver, Canada, October 24 - 28, 2004 ; proceedings","year":2004,"lang":"en","type":"article","venue":"","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Component (thermodynamics); Generative grammar; Relation (database); Generative Design","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002512608,0.001607428,0.001732034,0.001246339,0.001356862,0.005184812,0.001980128,0.001234461,0.03740305],"category_scores_gemma":[0.003714874,0.0009344501,0.001137869,0.002277099,0.00189393,0.002164792,0.002117118,0.003074922,0.007766924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003364284,"about_ca_system_score_gemma":0.004672169,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05171096,"about_ca_topic_score_gemma":0.09349576,"domain_scores_codex":[0.9991085,0.0002489221,0.00003316597,0.0002010532,0.000309261,0.00009908133],"domain_scores_gemma":[0.9980009,0.0007232883,0.00002920379,0.0003637305,0.0006775999,0.0002052895],"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":[0.000351914,0.0002521546,0.001062409,0.0003462362,0.0001423941,0.000207913,0.0004378434,0.01589993,0.003633671,0.05815984,0.5004143,0.4190913],"study_design_scores_gemma":[0.000180114,0.0001461186,0.002820562,0.0003807193,0.0001942689,0.0006137865,0.0004808618,0.1105783,0.006713355,0.114117,0.7636902,0.000084668],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01344204,0.04008172,0.8198212,0.01407871,0.01409273,0.0002245388,0.001176415,0.005992553,0.09109019],"genre_scores_gemma":[0.08629432,0.03314996,0.3022577,0.001447278,0.00312081,0.00027831,0.004797147,0.004220909,0.5644336],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.948289,"threshold_uncertainty_score":0.1251256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02429129952004159,"score_gpt":0.1839330015838521,"score_spread":0.1596417020638105,"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."}}