{"id":"W2066228674","doi":"10.1115/1.4025489","title":"Product Design Retrieval by Matching Bills of Materials","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Matching (statistics); Product (mathematics); Tree (set theory); Process (computing); Product design; Data mining; Engineering drawing; Industrial engineering; Engineering; Mathematics; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008510733,0.0001281576,0.0003210132,0.00008266458,0.00002396241,0.00005623364,0.0002030264,0.00008243944,0.0003556513],"category_scores_gemma":[0.0001478461,0.00009713961,0.00005288001,0.00009839707,0.000009729072,0.000289773,0.00001558625,0.0001504914,0.00001398319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003392212,"about_ca_system_score_gemma":0.00002470485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004531764,"about_ca_topic_score_gemma":2.123751e-8,"domain_scores_codex":[0.9987435,0.0001072881,0.0006132516,0.00008951062,0.0002771536,0.0001692732],"domain_scores_gemma":[0.9992774,0.0001276519,0.0002388429,0.0001168179,0.0001515078,0.00008778427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000728412,0.00003396098,2.502422e-7,0.0001073131,0.00004924307,0.000003030763,0.00006532147,0.1876834,0.8047636,0.00005992969,0.005678368,0.001482792],"study_design_scores_gemma":[0.0002638482,0.0001757894,0.000009593494,0.00009046794,0.0000261538,0.00002509816,0.00001187838,0.008266015,0.9865823,0.004366722,0.0000720123,0.00011017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04625581,0.0003231907,0.9526174,0.00009530578,0.0004165954,0.0002309737,0.000002311861,0.00003486826,0.00002352028],"genre_scores_gemma":[0.9004916,0.0002176939,0.09905665,0.00002278909,0.0001276668,0.000002596412,8.450315e-7,0.00003153042,0.00004863309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8542358,"threshold_uncertainty_score":0.3961238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01871402759967814,"score_gpt":0.2121901240888686,"score_spread":0.1934760964891905,"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."}}