{"id":"W2577240685","doi":"10.21700/ijcis.2016.129","title":"Comparison of Eight Proposed Security Methods using Linguistic Steganography Text","year":2016,"lang":"en","type":"article","venue":"International Journal of Computing and Information Sciences","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Steganography; Linguistics; Computer science; Natural language processing; Artificial intelligence; Philosophy; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00159233,0.00008691065,0.0001919822,0.000671735,0.0001344172,0.0001865201,0.0008931893,0.00003397451,0.000001562027],"category_scores_gemma":[0.0002425255,0.0000531434,0.00008323378,0.0003837794,0.0002607949,0.002675408,0.0001620518,0.00008695568,2.891791e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001922941,"about_ca_system_score_gemma":0.00007844364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004268266,"about_ca_topic_score_gemma":1.260696e-7,"domain_scores_codex":[0.998383,0.0001031217,0.0007608715,0.00008639093,0.0005462778,0.0001203709],"domain_scores_gemma":[0.997628,0.000310877,0.001083979,0.00008713836,0.0008396461,0.00005037582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005952765,0.0001200879,0.04409262,0.00005322684,0.0001211412,0.000003877436,0.01488176,0.001176292,0.007584868,0.1092744,0.0000819142,0.8225503],"study_design_scores_gemma":[0.001944931,0.001342663,0.01281277,0.001931688,0.00003582588,0.0005273904,0.0009239018,0.5999912,0.2224633,0.1377825,0.019566,0.0006778691],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1299326,0.00009070314,0.8685795,0.0002275819,0.0006440773,0.00003830235,0.000001314259,0.00002276547,0.0004631297],"genre_scores_gemma":[0.6827066,0.00002446021,0.3171755,0.00004778494,0.00004406586,7.984201e-8,1.400572e-7,8.948857e-7,4.857467e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8218725,"threshold_uncertainty_score":0.2167125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441448017752521,"score_gpt":0.4039754148331053,"score_spread":0.3695609346555802,"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."}}