{"id":"W1599062058","doi":"10.1007/11428831_85","title":"On a Generalized Demosaicking Procedure: A Taxonomy of Single-Sensor Imaging Solutions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Demosaicing; Artificial intelligence; Computer science; Image sensor; Image (mathematics); Image processing; Color image","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001257833,0.0005463447,0.0006537068,0.001091135,0.0003297714,0.0004370188,0.002493536,0.0002054817,0.00001908654],"category_scores_gemma":[0.0002603828,0.0005087129,0.0002202397,0.0007235549,0.0006622823,0.0005817814,0.0008874386,0.0006812118,0.00002065318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003581408,"about_ca_system_score_gemma":0.0006320853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001829098,"about_ca_topic_score_gemma":0.00001752134,"domain_scores_codex":[0.9960473,0.0000924663,0.0006880243,0.001419087,0.0009432276,0.0008099595],"domain_scores_gemma":[0.997121,0.0006935927,0.0004677627,0.001231504,0.0003329557,0.0001531819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001870992,0.00008016681,0.00001580218,0.00004876614,0.00001561764,0.00009111302,0.0004483325,0.03829899,0.006221353,0.02502901,0.00007334997,0.9296588],"study_design_scores_gemma":[0.001150181,0.0002826694,0.00003835527,0.001384476,0.00003034659,0.0002969353,2.79503e-7,0.8181041,0.02410054,0.1492132,0.004100826,0.001298095],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008954613,0.0007385325,0.9928132,0.0009051044,0.0008850605,0.0004783462,0.000004121302,0.0001459382,0.003940118],"genre_scores_gemma":[0.06504047,0.00001486697,0.9318023,0.00227736,0.0004727071,0.00001668276,0.000002585843,0.0000396302,0.0003333882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9283607,"threshold_uncertainty_score":0.9997364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0438991196364152,"score_gpt":0.2662725237639169,"score_spread":0.2223734041275017,"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."}}