{"id":"W2050037170","doi":"10.1109/mwscas.2011.6026510","title":"An adaptive bistable system based detector and its application in watermark extraction","year":2011,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Detector; Watermark; Gaussian noise; Noise (video); Bistability; Stochastic resonance; Computer science; SIGNAL (programming language); Gaussian; Amplitude; Digital watermarking; Additive white Gaussian noise; Signal-to-noise ratio (imaging); Pulse-amplitude modulation; Algorithm; Electronic engineering; Control theory (sociology); Physics; Pulse (music); Optics; Telecommunications; Artificial intelligence; Engineering; Optoelectronics; White noise; 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":[0.0003167354,0.00006593634,0.00006825769,0.0001246262,0.00003828325,0.00004671255,0.0001849161,0.00005271316,0.000004994852],"category_scores_gemma":[0.000002902907,0.00005870527,0.000009232918,0.0001693282,0.000007187933,0.000813242,0.0000223155,0.00005776537,0.00001270411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004318221,"about_ca_system_score_gemma":0.00002156431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000252918,"about_ca_topic_score_gemma":0.0001246107,"domain_scores_codex":[0.9993625,0.00008661694,0.0001329947,0.0002338448,0.00008606657,0.00009798976],"domain_scores_gemma":[0.9995968,0.00001769966,0.00005244225,0.0002344571,0.00005271488,0.000045849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001725602,0.0005145079,0.004377592,0.0001184515,0.00001179881,0.0000158001,0.00641084,0.0001864393,0.2535312,0.6766604,0.00008151567,0.05791889],"study_design_scores_gemma":[0.0001268205,0.0001081947,0.006063493,0.000009611269,0.000001132858,0.000004021514,0.00007312493,0.6743338,0.3188065,0.000288154,0.00009143383,0.00009376266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08581909,0.000009026852,0.909668,0.00002575469,0.00002030905,0.0002621427,4.945298e-7,0.000405451,0.003789724],"genre_scores_gemma":[0.9280241,8.963195e-7,0.07178825,0.00006580239,0.000005995817,0.0000822861,0.000001184417,0.000004310368,0.00002716608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.842205,"threshold_uncertainty_score":0.2393931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0291560956486077,"score_gpt":0.2570543818333837,"score_spread":0.227898286184776,"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."}}