{"id":"W4407141354","doi":"10.22541/au.173866897.72952916/v1","title":"Base64 Decoding with Ignorable Characters using SIMD instructions","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"SIMD; Decoding methods; Computer science; Parallel computing; Arithmetic; Mathematics; Algorithm","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":[0.0006020772,0.001440782,0.0007278003,0.001385674,0.0006401165,0.002089045,0.001347748,0.001103772,0.009310817],"category_scores_gemma":[0.004624474,0.0004835835,0.0005526838,0.001483386,0.000844337,0.002103356,0.00143733,0.001452442,0.009266065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006297478,"about_ca_system_score_gemma":0.001107772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008517157,"about_ca_topic_score_gemma":0.0008763324,"domain_scores_codex":[0.9989172,0.0001059043,0.0001066581,0.0002022004,0.0005612586,0.0001068049],"domain_scores_gemma":[0.9979351,0.0004913171,0.0001839945,0.00068453,0.0006208175,0.00008417603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008793087,0.0001157597,0.001414594,0.0003417872,0.00005978366,0.0004430823,0.0004241142,0.02009331,0.1165677,0.05356033,0.02090969,0.7851906],"study_design_scores_gemma":[0.000101242,0.0003034592,0.0005987438,0.0001621519,0.00005423971,0.000871343,0.0001293388,0.2996234,0.552708,0.04631965,0.09901238,0.0001160266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01734388,0.0004267189,0.9593558,0.0001362091,0.0002788322,0.00008637169,0.0002378793,0.0138414,0.008292857],"genre_scores_gemma":[0.2040274,0.0005820792,0.7680564,0.0004097107,0.0002214536,0.0002273031,0.001324805,0.002664624,0.02248629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009310817,"threshold_uncertainty_score":0.03114778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187641165965092,"score_gpt":0.2814897438929081,"score_spread":0.2496133322332572,"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."}}