{"id":"W2010792847","doi":"10.1109/icdar.2013.105","title":"A Probabilistic Model for Reconstruction of Torn Forensic Documents","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Probabilistic logic; Computer science; Set (abstract data type); Process (computing); Subject (documents); Data mining; Artificial intelligence; Order (exchange); Information retrieval; World Wide Web","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":[0.002943332,0.0008840347,0.001329158,0.002602467,0.0008776243,0.002786494,0.003467722,0.002584169,0.003668272],"category_scores_gemma":[0.009426329,0.001380332,0.001631135,0.00195963,0.002201799,0.004065138,0.001499444,0.002597452,0.001418071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00161813,"about_ca_system_score_gemma":0.001250186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006255726,"about_ca_topic_score_gemma":0.005439423,"domain_scores_codex":[0.998503,0.0003976733,0.00009300938,0.000428792,0.0004562561,0.000121376],"domain_scores_gemma":[0.9959574,0.002466507,0.0005019868,0.0005133806,0.0004536061,0.0001069923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009727884,0.00003717927,0.001046358,0.0001154402,0.0000604494,0.000193602,0.0001555793,0.9094909,0.002285926,0.05515094,0.0009268635,0.03043942],"study_design_scores_gemma":[0.00000649816,0.00001435717,0.0001759476,0.00001200186,0.0000101847,0.00009253429,0.00001568042,0.9787832,0.0005153901,0.01959335,0.0007582598,0.00002251338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006361561,0.0001424298,0.9922273,0.0001996867,0.00001818634,0.00003119967,0.0001507487,0.0002636337,0.0006053487],"genre_scores_gemma":[0.4274368,0.001310599,0.5592991,0.0002615421,0.0002056303,0.000454894,0.001347461,0.0003921125,0.009291867],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006255726,"threshold_uncertainty_score":0.01556605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151041566781653,"score_gpt":0.2344864607945041,"score_spread":0.2193823041163388,"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."}}