{"id":"W1998758349","doi":"10.1016/j.cad.2009.08.004","title":"Assisting designer using feature modeling for lifecycle","year":2009,"lang":"en","type":"article","venue":"Computer-Aided Design","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Product lifecycle; Computer science; Product design specification; Process (computing); Kernel (algebra); Product (mathematics); Product design; Systems engineering; Product engineering; Engineering design process; New product development; Feature (linguistics); Industrial engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003592303,0.0006857347,0.0005815246,0.0005199869,0.0003903083,0.0007437699,0.0007678549,0.0005892841,0.005594995],"category_scores_gemma":[0.001485505,0.0004689517,0.0006457074,0.0004076825,0.0001724651,0.0009906773,0.000519101,0.0004372452,0.001008594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027523,"about_ca_system_score_gemma":0.0005924851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002487306,"about_ca_topic_score_gemma":0.004810093,"domain_scores_codex":[0.999773,0.00005569707,0.00001053236,0.00003157376,0.000104538,0.00002481162],"domain_scores_gemma":[0.9995765,0.0001929801,0.00004342705,0.00008963426,0.00008832206,0.000009219138],"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":[0.0002458207,0.0001351856,0.00329813,0.0002898718,0.00003723002,0.0002907931,0.0003514581,0.688552,0.04114473,0.009712004,0.00367435,0.2522684],"study_design_scores_gemma":[0.000008031272,0.00004361793,0.0002782326,0.000009088074,0.00001725732,0.00006808637,0.00003312381,0.9856029,0.009095623,0.001285784,0.003547909,0.00001044457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02575849,0.00004389253,0.9669504,0.00004077079,0.00001212477,0.00005177518,0.0001771308,0.002527508,0.004437865],"genre_scores_gemma":[0.5532788,0.000128345,0.4416753,0.00002534965,0.00000578727,0.000122222,0.000550146,0.0004321888,0.00378178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005594995,"threshold_uncertainty_score":0.01871711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05187074196723367,"score_gpt":0.2465113889337196,"score_spread":0.1946406469664859,"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."}}