{"id":"W3121601712","doi":"10.7146/hn.v5i2.142742","title":"Interfacing the Hebrew Bible: past, present and future applications for the BHSA","year":2019,"lang":"en","type":"article","venue":"HIPHIL Novum","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Learning Partnership","funders":"","keywords":"Linguistics; Hebrew; Computer science; Hebrew Bible; Syntax; Biblical languages; Grammar; Semantics (computer science); Interface (matter); Interpretation (philosophy); Artificial intelligence; Biblical studies; Literature; Philosophy; Art; Programming language","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":[0.003485269,0.0006344365,0.0003607432,0.0009596248,0.0008174789,0.003795487,0.001169172,0.002048913,0.0367586],"category_scores_gemma":[0.00459113,0.0003876352,0.0004790534,0.001126422,0.00150295,0.007641089,0.002866545,0.001683443,0.00785712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008007475,"about_ca_system_score_gemma":0.000660052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002028764,"about_ca_topic_score_gemma":0.002662829,"domain_scores_codex":[0.9987783,0.000539905,0.00008822283,0.0001210226,0.0003810658,0.00009146477],"domain_scores_gemma":[0.997931,0.001006536,0.00004252193,0.0003123116,0.0003661059,0.0003416821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005272471,0.0002133365,0.002224854,0.00098463,0.00002983645,0.0007065156,0.01600992,0.0007130681,0.03329304,0.04881043,0.03948268,0.8570045],"study_design_scores_gemma":[0.0000863376,0.0004113937,0.004437034,0.001401461,0.0000452081,0.00173803,0.00822611,0.009385414,0.01135957,0.03887402,0.9239013,0.0001342234],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1438428,0.0434743,0.5374395,0.03200256,0.002910294,0.0006258278,0.001384162,0.01700134,0.2213192],"genre_scores_gemma":[0.2638119,0.01998244,0.5626865,0.002826555,0.001221879,0.000396464,0.003201912,0.002995761,0.1428766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0367586,"threshold_uncertainty_score":0.1229697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000349099667668,"score_gpt":0.2611827599101989,"score_spread":0.2511792689135222,"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."}}