{"id":"W2339619249","doi":"10.1073/pnas.1522200113","title":"Algorithmic handwriting analysis of Judah’s military correspondence sheds light on composition of biblical texts","year":2016,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Research Council; Tel Aviv University; Azrieli Foundation; Israel Science Foundation","keywords":"Literacy; Fortress (chess); Handwriting; Demise; Composition (language); Biblical studies; Literature; History; Computer science; Ancient history; Archaeology; Art; Political science; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002079076,0.0001198641,0.0003337029,0.00187604,0.0001231584,0.00001699605,0.001782073,0.0001016608,0.0000377369],"category_scores_gemma":[0.0004789884,0.0000727129,0.0002051574,0.00528636,0.0008136742,0.0009250519,0.000277271,0.0001168258,0.000001701388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004160644,"about_ca_system_score_gemma":0.00004706117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008436227,"about_ca_topic_score_gemma":1.167038e-7,"domain_scores_codex":[0.9970292,0.0000276631,0.0006646584,0.0003732008,0.001732013,0.0001732704],"domain_scores_gemma":[0.9979264,0.0004998215,0.0006811713,0.0000311753,0.0008146387,0.00004685248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002291696,0.0001291513,0.002943914,0.00004653023,0.00006940514,1.452807e-8,0.0001677183,0.00001882938,0.9417456,0.04362667,0.0004328951,0.01079639],"study_design_scores_gemma":[0.0001513964,0.0001520769,0.0483984,0.0003530272,0.00004326752,0.000004145943,0.00002537023,0.007942377,0.9155348,0.0272803,0.00002217159,0.00009265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851758,0.0001344042,0.003910111,0.006320517,0.00003338708,0.000318766,0.0000721714,0.00006368433,0.003971165],"genre_scores_gemma":[0.984736,0.00005142817,0.01497763,0.0001580144,0.00002551367,0.00001097213,1.847868e-7,0.000002883472,0.00003740535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04545449,"threshold_uncertainty_score":0.3311564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02718084531866138,"score_gpt":0.3010477378835328,"score_spread":0.2738668925648714,"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."}}