{"id":"W31253635","doi":"10.1242/dev.178400","title":"Σχεδίαση και ανάπτυξη υδατογραφικού σχήματος για σήματα ηλεκτροεγκεφαλογραφήματος","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Alberta Children's Hospital Research Institute; University of Calgary","keywords":"Digital watermarking; Watermark; Computer science; Distortion (music); SIGNAL (programming language); Noise (video); White noise; Artificial intelligence; Computer vision; Telecommunications; Image (mathematics); Bandwidth (computing)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073796,0.0004468458,0.0003982882,0.001172758,0.00103331,0.002015964,0.0005483344,0.001339858,0.02712666],"category_scores_gemma":[0.002272407,0.0004649442,0.0003426844,0.0006883597,0.001712536,0.002212924,0.001310208,0.002004001,0.006312971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008627351,"about_ca_system_score_gemma":0.001137128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008441,"about_ca_topic_score_gemma":0.001497371,"domain_scores_codex":[0.9990008,0.0001505709,0.00005201573,0.0002101777,0.0004609475,0.0001253245],"domain_scores_gemma":[0.9986596,0.0004263898,0.0002281163,0.0001435636,0.0003696698,0.0001727205],"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":[0.0004480152,0.0003147368,0.005062818,0.001403348,0.00008178828,0.001388206,0.004263258,0.002182749,0.4825271,0.08706526,0.01777028,0.3974924],"study_design_scores_gemma":[0.00009462641,0.0005123719,0.01857123,0.001391312,0.0001163221,0.00294601,0.006063149,0.003666841,0.144066,0.08105105,0.74128,0.0002411372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2711999,0.04978544,0.2081173,0.02330713,0.003476827,0.0004845071,0.001860947,0.001391653,0.4403764],"genre_scores_gemma":[0.8037297,0.03150756,0.06276409,0.0032189,0.0008793051,0.000462803,0.0007260306,0.0004360782,0.09627561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02712666,"threshold_uncertainty_score":0.09074777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261773986457897,"score_gpt":0.3005151765319568,"score_spread":0.2778974366673778,"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."}}