{"id":"W2617918738","doi":"10.16995/dm.25","title":"Image Acquisition & Processing Routines for Damaged Manuscripts","year":2011,"lang":"en","type":"article","venue":"Digital Medievalist","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Decipherment; Computer science; Artificial intelligence; Optical character recognition; Character (mathematics); Document processing; Computer graphics (images); Image processing; Computer vision; Document image processing; Natural language processing; Pattern recognition (psychology); Information retrieval; Image (mathematics); Image segmentation; Linguistics","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":[0.0002002126,0.0001645691,0.0001541474,0.0001018348,0.0002533192,0.0008209801,0.0004350721,0.00006017212,0.00001041922],"category_scores_gemma":[0.0001424292,0.0001471495,0.00007615142,0.000215619,0.0001417045,0.00302814,0.00009304947,0.00008372116,0.00005429653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002774538,"about_ca_system_score_gemma":0.00007533633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006790724,"about_ca_topic_score_gemma":0.000003124395,"domain_scores_codex":[0.9988381,0.00001677293,0.0002496628,0.0003836346,0.0002056747,0.0003061001],"domain_scores_gemma":[0.9991748,0.00002799117,0.0001417201,0.0002822232,0.0002767452,0.00009651891],"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":[0.00008834568,0.0002448442,0.00155595,0.0004965687,0.00002880849,0.0000183245,0.004397738,4.866795e-7,0.005616821,0.003036826,0.00445979,0.9800555],"study_design_scores_gemma":[0.01416979,0.00185355,0.0439368,0.003223035,0.0003934635,0.003417749,0.003383342,0.143016,0.1826576,0.4667241,0.1283444,0.008880167],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01895828,0.0001587418,0.9457653,0.000296687,0.000956447,0.0002160713,0.00002150381,0.0004681093,0.03315887],"genre_scores_gemma":[0.7784928,0.000002376604,0.2194611,0.0001826271,0.0002531985,0.00004073141,0.00006033425,0.00002111438,0.00148572],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9711753,"threshold_uncertainty_score":0.7916731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0464016831851975,"score_gpt":0.2606903526211621,"score_spread":0.2142886694359646,"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."}}