{"id":"W2367797865","doi":"","title":"Form and Position Errors Computation of Geometrical Products Using Genetic Algorithm","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Position (finance); Key (lock); Genetic algorithm; Algorithm; Fitness function; Computation; Field (mathematics); Mathematical optimization; Function (biology); Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004318284,0.00008057769,0.0001049812,0.0001820144,0.00004092455,0.00000735093,0.00005148898,0.00004554469,4.864044e-7],"category_scores_gemma":[3.362184e-7,0.00008815212,0.00001695896,0.0003310597,0.0000230136,0.0000654648,0.00001025163,0.00005772065,9.195022e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003732656,"about_ca_system_score_gemma":0.000005584412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001232219,"about_ca_topic_score_gemma":8.310509e-8,"domain_scores_codex":[0.9995396,0.000005281226,0.0001621251,0.0001287139,0.00005708136,0.0001072003],"domain_scores_gemma":[0.9997845,0.000007521757,0.0000342056,0.00007707869,0.00006888829,0.00002781188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001192959,0.00004383071,0.00008773297,0.00002728457,0.00001216617,2.733367e-7,0.00005193791,0.004187249,0.1796978,0.0001250469,0.00008840895,0.8156771],"study_design_scores_gemma":[0.001025797,0.0003197267,0.07568661,0.00007232213,0.0001473151,0.0001530158,0.00001879823,0.4557296,0.4288425,0.01973627,0.01755904,0.0007090681],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0651978,0.0004477034,0.9337062,0.00004126902,0.000007692762,0.0003805386,0.000003146843,0.0001630225,0.00005259092],"genre_scores_gemma":[0.2754681,0.00002729109,0.7243897,0.00002935017,0.00004542024,0.00002007947,0.00001140909,0.000006816481,0.00000186365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.814968,"threshold_uncertainty_score":0.3594739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279181673905913,"score_gpt":0.2494290082997232,"score_spread":0.2366371915606641,"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."}}