{"id":"W1993582662","doi":"10.1049/el:20031198","title":"Digital camera zooming on colour filter array","year":2003,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Zoom; Artificial intelligence; Computer science; Noise (video); Filter (signal processing); Enhanced Data Rates for GSM Evolution; Color filter array; Adaptive filter; Digital filter; Image sensor; Digital camera; Computer graphics (images); Image (mathematics); Engineering; Color gel; Algorithm","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":[0.0001815857,0.0001738327,0.0001275336,0.0001020124,0.0001033256,0.0003107129,0.0005888783,0.00003893008,0.00002278986],"category_scores_gemma":[0.00004694891,0.0001738441,0.0000660542,0.0002376932,0.00003026582,0.0005712952,0.00004011855,0.0002830906,0.0001439037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230248,"about_ca_system_score_gemma":0.00006436505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.762985e-7,"about_ca_topic_score_gemma":0.00000106475,"domain_scores_codex":[0.9985418,0.00004776737,0.0001606126,0.0003865133,0.0002704791,0.0005927678],"domain_scores_gemma":[0.9992852,0.00005975558,0.00006824168,0.0005111346,0.00002298357,0.00005269082],"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":[0.00001337707,0.0001698275,0.000260987,0.00001108794,0.00006436613,0.00007224621,0.0004472597,0.00005130618,0.7731054,0.09958693,0.08598956,0.04022761],"study_design_scores_gemma":[0.0003363508,0.0003219093,0.00005022761,0.00002173865,0.000004407256,0.00002682835,0.00000535537,0.0006095379,0.7312409,0.001520084,0.265397,0.0004656906],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03786246,0.0000891218,0.9394562,0.004859433,0.000221372,0.0002016965,9.099496e-7,0.0004294262,0.01687943],"genre_scores_gemma":[0.9304666,0.00001503498,0.05106958,0.01702111,0.00007039508,0.00004824992,0.000004048853,0.00003131207,0.001273651],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8926042,"threshold_uncertainty_score":0.7089155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006350863864631526,"score_gpt":0.2132619072278155,"score_spread":0.2069110433631839,"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."}}