{"id":"W4407194208","doi":"10.1007/s00521-024-10546-y","title":"Optimizing a continuous action learning automata (CALA) optimizer for training artificial neural networks","year":2025,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computational Science and Engineering; Computer science; Artificial neural network; Training (meteorology); Action (physics); Artificial intelligence; Automaton; Machine learning; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001611561,0.0009573528,0.00112634,0.0005798202,0.0006220203,0.001127082,0.001473102,0.002714046,0.004097465],"category_scores_gemma":[0.005694297,0.0005570028,0.0007470509,0.0005470868,0.001155129,0.0008415871,0.001184931,0.002085974,0.0006502873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122944,"about_ca_system_score_gemma":0.001670011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00917709,"about_ca_topic_score_gemma":0.009730984,"domain_scores_codex":[0.9993398,0.0002400329,0.00004610016,0.0001744549,0.000130397,0.00006923622],"domain_scores_gemma":[0.9975219,0.001622115,0.0001141436,0.0001925765,0.0004607644,0.00008836137],"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":[0.00009384362,0.00005798467,0.0005141696,0.00007234438,0.00003744536,0.00004361213,0.00005147014,0.9455946,0.001250435,0.006854065,0.001373327,0.04405672],"study_design_scores_gemma":[0.000004820384,0.00001382057,0.00002064226,0.000003330457,0.000002831157,0.000002903713,0.000002406925,0.9990858,0.0001597223,0.0005887771,0.0001136202,0.000001350495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04358573,0.0005152219,0.9462571,0.0004638106,0.0002736493,0.0001213857,0.0001180986,0.001268764,0.007396268],"genre_scores_gemma":[0.6720541,0.0001422449,0.3225451,0.0003019921,0.0000850025,0.0004010732,0.0002040267,0.0002549155,0.004011598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00917709,"threshold_uncertainty_score":0.01824737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05010621640168843,"score_gpt":0.3288892452449029,"score_spread":0.2787830288432144,"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."}}