{"id":"W1809620427","doi":"","title":"Invariant Image Improvement by sRGB colour space sharpening","year":2005,"lang":"en","type":"article","venue":"UEA Digital Repository (University of East Anglia)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sharpening; Artificial intelligence; Computer vision; Invariant (physics); Mathematics; Color image; Color space; Computer science; Image processing; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000959071,0.0001692868,0.000195321,0.00009407561,0.0002130784,0.0002735635,0.00101433,0.0000666881,0.00002204672],"category_scores_gemma":[0.00001726908,0.0002153204,0.0001147925,0.0002375323,0.0001285635,0.00324206,0.0006824359,0.0001218227,0.00007017003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001665886,"about_ca_system_score_gemma":0.00006874647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008978225,"about_ca_topic_score_gemma":0.000009605285,"domain_scores_codex":[0.9986991,0.00002074573,0.0001645019,0.000430731,0.000385153,0.0002997284],"domain_scores_gemma":[0.9989529,0.00002265282,0.0002228025,0.0005124444,0.0001569266,0.0001323344],"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":[0.00009619845,0.0008597299,0.001323364,0.00007586434,0.0001505222,0.0005753707,0.00330683,0.00001775244,0.7929201,0.007197357,0.1163402,0.07713677],"study_design_scores_gemma":[0.003064394,0.001912428,0.002403233,0.0003337016,0.00008973261,0.0001739471,0.004805561,0.02809669,0.8170898,0.0007359796,0.1392219,0.002072605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1883063,0.0001841936,0.71595,0.002035667,0.0002597437,0.0005183105,0.00004265266,0.0007646452,0.09193859],"genre_scores_gemma":[0.9396035,0.000006520836,0.04683182,0.00006575645,0.00005079072,8.569551e-7,0.000009104729,0.0000119631,0.01341968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7512972,"threshold_uncertainty_score":0.8780511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005327444168061117,"score_gpt":0.1750379053739109,"score_spread":0.1697104612058498,"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."}}