{"id":"W2950849703","doi":"","title":"Authorship Proof for Textual Document.","year":2007,"lang":"en","type":"preprint","venue":"IACR Cryptology ePrint Archive","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scheme (mathematics); Digital watermarking; Natural (archaeology); Information retrieval; Theoretical computer science; Computer security; Image (mathematics); Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001155116,0.0005937741,0.000707929,0.000680954,0.0002651544,0.0001654755,0.00294722,0.0006513019,0.00001050448],"category_scores_gemma":[0.0001605378,0.0005727623,0.0005252098,0.0002053543,0.0004432952,0.0002081471,0.003900459,0.001500422,0.00001872343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008144783,"about_ca_system_score_gemma":0.0001816045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000146446,"about_ca_topic_score_gemma":0.00003117983,"domain_scores_codex":[0.9961566,0.0002944621,0.0007237142,0.001506906,0.0003015914,0.001016769],"domain_scores_gemma":[0.9966891,0.0007319796,0.0004914689,0.00169668,0.0001583872,0.000232413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009255629,0.00007717002,0.0005138693,0.0001510554,0.0001093547,0.00004977097,0.001529396,0.0001103439,0.0001726166,0.9554096,0.0008919448,0.04089227],"study_design_scores_gemma":[0.0003097053,0.0001834599,0.0005765836,0.0001478616,0.00002699634,0.0000470169,0.00001880951,0.001424076,0.01077658,0.968419,0.01746231,0.0006076659],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002379925,0.0001686402,0.9868602,0.00128774,0.001029787,0.001762657,0.0000515724,0.0009517417,0.005507804],"genre_scores_gemma":[0.0705018,0.00004793733,0.9279586,0.0003588607,0.0002502799,0.0007465361,0.0000836547,0.00003964545,0.00001269385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06812188,"threshold_uncertainty_score":0.9996724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02805405024568698,"score_gpt":0.3158970230375547,"score_spread":0.2878429727918677,"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."}}