{"id":"W2288749932","doi":"10.1109/apcc.2015.7412529","title":"Design of new fingerprinting codes using optical orthogonal codes","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fingerprint (computing); Fingerprint recognition; Computer science; Collusion; Binary number; Algorithm; Feature (linguistics); Code (set theory); Matrix (chemical analysis); Pattern recognition (psychology); Mathematics; Artificial intelligence; Arithmetic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000386206,0.0005052352,0.0003503952,0.0007310876,0.0003290749,0.000481142,0.0005618985,0.0006019372,0.0006359472],"category_scores_gemma":[0.001814799,0.0002493992,0.0002767098,0.0006540352,0.0004145978,0.0006980614,0.0005247982,0.0004197407,0.0002484809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005181553,"about_ca_system_score_gemma":0.0006040616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000772146,"about_ca_topic_score_gemma":0.0007673739,"domain_scores_codex":[0.9994045,0.000123483,0.0000457902,0.0001123325,0.0002352143,0.000078668],"domain_scores_gemma":[0.9984823,0.000320581,0.0004244312,0.0001953949,0.000490195,0.00008711265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006319749,0.0002078126,0.003453696,0.0003249024,0.0001000467,0.0004586228,0.0002625641,0.1218322,0.2987674,0.06898455,0.00221262,0.5027636],"study_design_scores_gemma":[0.0001430471,0.000920218,0.001253662,0.00006207261,0.0000696386,0.001567196,0.00005655846,0.797785,0.172656,0.009396008,0.01598275,0.0001078196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08528404,0.0005364535,0.9104612,0.0001594963,0.00009320386,0.000115782,0.00006722756,0.0003817594,0.002900876],"genre_scores_gemma":[0.6013154,0.0004569541,0.3954898,0.0001116173,0.0000525354,0.0001509989,0.00008330129,0.00002547596,0.002313841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000772146,"threshold_uncertainty_score":0.003759503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0922784969796045,"score_gpt":0.307414147032683,"score_spread":0.2151356500530786,"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."}}