{"id":"W4238608736","doi":"10.1167/18.10.137","title":"Spatial frequency tuning for outdoor scene categorization","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Victoria","funders":"","keywords":"Categorization; Computer science; Spatial frequency; Artificial intelligence; Computer vision; Cartography; Geography; Optics; Physics","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":[0.0005463304,0.00007463891,0.0001374375,0.0001650161,0.00007005678,0.00002858047,0.00009465813,0.00007552429,0.00005426781],"category_scores_gemma":[0.0004606536,0.00006367239,0.00006581032,0.0001032293,0.00002039244,0.0002503784,0.000009314283,0.000119215,0.000009785251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007270691,"about_ca_system_score_gemma":0.00001974152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003535467,"about_ca_topic_score_gemma":0.000001869211,"domain_scores_codex":[0.9993384,0.00003736566,0.0003129578,0.00005401575,0.0001357891,0.0001214941],"domain_scores_gemma":[0.9993476,0.00009660167,0.0001292701,0.00007679696,0.000310904,0.00003879403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006230798,0.00001079063,0.0002970397,0.00003634725,0.00002832745,0.000003689612,0.0002887679,0.00333305,0.9258705,0.00009325533,0.001178079,0.06879783],"study_design_scores_gemma":[0.001660332,0.002492886,0.01802974,0.0001936673,0.00007005801,0.0001303762,0.0001984585,0.2279778,0.7262809,0.01662942,0.005962342,0.0003740093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1621301,0.0001069624,0.8345997,0.00004931628,0.002638202,0.0000637128,0.000001771632,0.00005541139,0.0003547962],"genre_scores_gemma":[0.7045211,0.00002604624,0.294259,0.00001742125,0.001136295,0.000001121207,0.000001214805,0.00001784314,0.00002000165],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5423909,"threshold_uncertainty_score":0.2596485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03108858156556261,"score_gpt":0.3078625311393065,"score_spread":0.2767739495737438,"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."}}