{"id":"W2168115495","doi":"10.1142/s0218001407005284","title":"DETERMINISTIC AND EVOLUTIONARY EXTRACTION OF DELTA-LOGNORMAL PARAMETERS: PERFORMANCE COMPARISON","year":2007,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Evolutionary algorithm; Log-normal distribution; Noise (video); Handwriting; Artificial intelligence; Algorithm; Kinematics; Pattern recognition (psychology); Mathematics; Statistics","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":[0.0003948114,0.00008666683,0.0001323586,0.0001865063,0.00006524003,0.00008031231,0.0002675469,0.00004260833,0.00001666941],"category_scores_gemma":[0.00004239138,0.00008004797,0.00004645194,0.0001092872,0.000103747,0.0005168665,0.00006489667,0.0001592714,0.000005782607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001985844,"about_ca_system_score_gemma":0.00002017857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001349297,"about_ca_topic_score_gemma":0.00001376598,"domain_scores_codex":[0.9987713,0.00002816867,0.0006656671,0.0001385267,0.0002788911,0.0001175034],"domain_scores_gemma":[0.9987304,0.0002445792,0.0004703013,0.00006863313,0.0004046784,0.00008143792],"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":[0.0000489347,0.00009710608,0.002910734,0.000007462923,0.0000191798,0.00001411417,0.0002115645,0.0002715931,0.001674536,0.0003914876,0.00001486286,0.9943385],"study_design_scores_gemma":[0.0004368372,0.001490727,0.07406493,0.0007547677,0.0000861403,0.00313125,0.001344167,0.6765747,0.1774839,0.06317095,0.0007506579,0.0007109604],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5185229,0.00006120803,0.4808252,0.0001977015,0.0003160582,0.00003468831,0.000004935215,0.000005703503,0.00003157089],"genre_scores_gemma":[0.9908296,0.0002206177,0.008642394,0.0001364465,0.0001578222,0.00000162751,0.000004839351,0.000003585146,0.000003006976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9936275,"threshold_uncertainty_score":0.3264261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001592034183081,"score_gpt":0.3437041253537559,"score_spread":0.2435449219354478,"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."}}