{"id":"W4406140020","doi":"10.36227/techrxiv.173627343.33011671/v1","title":"Decrypting Caesar Ciphers using Machine Learning Regression: An exploratory analysis","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Exploratory analysis; Computer science; Machine learning; Artificial intelligence; Regression; Regression analysis; Statistics; Data science; Mathematics","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":[0.00232233,0.0007782199,0.0006792946,0.001066628,0.0003117001,0.0008577948,0.0005495263,0.0006015798,0.001175681],"category_scores_gemma":[0.008175551,0.0002927353,0.0009160012,0.0006156443,0.0006946105,0.001531538,0.0005789389,0.00137946,0.0004229381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005911999,"about_ca_system_score_gemma":0.0003655735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001213013,"about_ca_topic_score_gemma":0.0007160784,"domain_scores_codex":[0.9990904,0.0004466216,0.00004348665,0.00009342041,0.0002681676,0.00005781249],"domain_scores_gemma":[0.9943073,0.004223457,0.0003488568,0.000448779,0.000618596,0.00005296744],"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":[0.0005572044,0.0005077063,0.02464414,0.0004031966,0.0002216036,0.0006978099,0.0008992201,0.6790996,0.02703736,0.06611368,0.003365417,0.1964531],"study_design_scores_gemma":[0.000007882229,0.00009654428,0.001685165,0.00001784812,0.00001342464,0.00007257666,0.0000556611,0.9807139,0.008028924,0.008489358,0.0008021891,0.00001654714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.443217,0.001357314,0.5475215,0.001531666,0.00004797193,0.000206183,0.0002746323,0.0008734651,0.004970304],"genre_scores_gemma":[0.8765716,0.0007761672,0.1188018,0.00009078967,0.00008231909,0.00008319577,0.0003324023,0.0001081909,0.00315358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00232233,"threshold_uncertainty_score":0.01228184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05058368023047253,"score_gpt":0.3193808609049354,"score_spread":0.2687971806744628,"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."}}