{"id":"W2050427006","doi":"10.1016/j.cmpb.2010.08.016","title":"A contextual based double watermarking of PET images by patient ID and ECG signal","year":2010,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Digital watermarking; Artificial intelligence; Robustness (evolution); Computer science; Computer vision; Peak signal-to-noise ratio; Wavelet; Watermark; Pixel; Image quality; Pattern recognition (psychology); 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":[0.001090807,0.0002073117,0.0003683488,0.0002491055,0.00007277163,0.00008153397,0.0003414742,0.00007196348,0.000002424835],"category_scores_gemma":[0.000005600859,0.0001510184,0.00004101805,0.0003497139,0.0004365248,0.0002053577,0.0002944008,0.000260968,6.788063e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004992413,"about_ca_system_score_gemma":0.00001379866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005524985,"about_ca_topic_score_gemma":0.000002363901,"domain_scores_codex":[0.9985605,0.0001694076,0.0003896895,0.0004259155,0.000164693,0.0002897538],"domain_scores_gemma":[0.9991711,0.0001969142,0.0001426497,0.0003094578,0.00006122194,0.0001186645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003865731,0.00009782813,0.003749867,0.00006710709,0.00001073645,0.00002225754,0.0005290292,3.585691e-7,0.03240109,0.0004101768,0.00009557135,0.9625773],"study_design_scores_gemma":[0.01200686,0.008976199,0.007219174,0.001524152,0.00007485902,0.0006633602,0.0001832532,0.3034923,0.5172195,0.02952647,0.1170726,0.002041277],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08290114,0.0004623626,0.9155634,0.0003355299,0.0002616185,0.0003163993,0.000003623415,0.0001174038,0.00003848954],"genre_scores_gemma":[0.3889495,0.00002572599,0.6108062,0.0001518083,0.00002877944,0.00002063,0.000009496952,0.000005918861,0.000001884599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9605361,"threshold_uncertainty_score":0.6158352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02251937071661735,"score_gpt":0.3159155098594993,"score_spread":0.2933961391428819,"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."}}