{"id":"W4391938566","doi":"10.7146/hn.v5i2.142739","title":"Tyndale STEPBible Data development for machine analysis and computational linguistics","year":2019,"lang":"en","type":"article","venue":"HIPHIL Novum","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tyndale University","funders":"","keywords":"Computer science; Linguistics; Natural language processing; Philosophy","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":[0.002586547,0.0008285033,0.0009847423,0.006792376,0.001884208,0.003872686,0.002079993,0.001022688,0.1945809],"category_scores_gemma":[0.02165979,0.001038047,0.0008579317,0.008854646,0.0008630239,0.003866956,0.005088573,0.002815964,0.1690896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275366,"about_ca_system_score_gemma":0.004601329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009341516,"about_ca_topic_score_gemma":0.02019553,"domain_scores_codex":[0.9974754,0.0003818428,0.0002655379,0.00050125,0.001131729,0.0002441721],"domain_scores_gemma":[0.9859954,0.003458453,0.0007317751,0.004159473,0.004265043,0.001389895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003332332,0.00008663468,0.002195795,0.0003864293,0.00001945142,0.0001970028,0.0002591807,0.0002383725,0.00114159,0.008682526,0.9459137,0.04054606],"study_design_scores_gemma":[0.00007651961,0.0000322441,0.003592439,0.0001608266,0.000008391753,0.0001607334,0.0001476356,0.0006643924,0.002370041,0.004110195,0.9886419,0.00003460055],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.005062789,0.0003224999,0.02167769,0.001258303,0.000551642,0.0008389557,0.8784337,0.01409874,0.07775569],"genre_scores_gemma":[0.009748328,0.0002370147,0.03952155,0.0003350633,0.0001078491,0.003602414,0.8816726,0.0074792,0.05729599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1945809,"threshold_uncertainty_score":0.6509381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04036979840689944,"score_gpt":0.2970036244541125,"score_spread":0.2566338260472131,"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."}}