{"id":"W4414361672","doi":"10.1101/2025.09.18.677139","title":"Efficient Coding of Spatial Frequency in Natural Images: Cross-frequency Dependence","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scrambling; Redundancy (engineering); Coding (social sciences); Perception; Pattern recognition (psychology); Visual processing; Human visual system model; Spatial frequency; Visual perception","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":[0.0003027901,0.0001466346,0.0001515245,0.0003115096,0.00008198294,0.0003588712,0.0001213073,0.0001583376,0.001126286],"category_scores_gemma":[0.002338226,0.0001595478,0.0001002191,0.0001458918,0.0002980633,0.0004674884,0.0002907748,0.0002277803,0.000140176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001473017,"about_ca_system_score_gemma":0.000124663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004106929,"about_ca_topic_score_gemma":0.0004850539,"domain_scores_codex":[0.999831,0.00003538707,0.00001017572,0.00003791486,0.00006325555,0.00002229439],"domain_scores_gemma":[0.9987636,0.0007500383,0.0001752154,0.0001094862,0.0001582744,0.00004348842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000240947,0.00005122892,0.003684904,0.00007295694,0.00001685064,0.00006159041,0.000094737,0.001036417,0.9755124,0.0005130665,0.0001161505,0.0185987],"study_design_scores_gemma":[0.00003471688,0.0003517279,0.4566269,0.00001921093,0.00004418206,0.0006652762,0.0001263517,0.04933232,0.48891,0.002679279,0.001166383,0.00004366299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829746,0.0001229503,0.01503571,0.00003075283,0.000005286387,0.00001341581,0.00005133257,0.00006379023,0.001702183],"genre_scores_gemma":[0.9934203,0.00006343976,0.006007502,0.00002301524,0.000004450692,0.00001047189,0.00006165063,0.00002912878,0.0003800245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001126286,"threshold_uncertainty_score":0.003767788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008733340821811935,"score_gpt":0.241675823183636,"score_spread":0.232942482361824,"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."}}