{"id":"W4394031514","doi":"10.5281/zenodo.10462727","title":"Grammatical functions, inflectional class and textual frequency in Russian nominals","year":2015,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Discourse Analysis and Cultural Communication","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Economic and Social Research Council","keywords":"Class (philosophy); Linguistics; Natural language processing; Computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001568032,0.0001440293,0.0002053207,0.0002863169,0.002341217,0.001057092,0.0008845481,0.0001810142,0.01116374],"category_scores_gemma":[0.0009488164,0.0001413167,0.00005047015,0.0006593263,0.0004046998,0.0003231655,0.0005579847,0.0004906853,0.003472153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003210732,"about_ca_system_score_gemma":0.0000288885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001407254,"about_ca_topic_score_gemma":0.0005397525,"domain_scores_codex":[0.9975658,0.0009348977,0.0003159241,0.0003234026,0.0005647369,0.0002952442],"domain_scores_gemma":[0.9988273,0.00003483121,0.0001514749,0.000394432,0.0003769497,0.0002149745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001221297,0.00009683632,0.000001993292,0.00001698436,0.00002827762,0.000003236313,0.0006184941,0.000004083992,0.000007914085,0.008944828,0.982479,0.007786102],"study_design_scores_gemma":[0.0001747704,0.00006970848,0.0001776887,0.00003667126,0.00003733035,0.00001037861,0.003044809,0.00001860684,3.302507e-7,0.0009532968,0.995313,0.000163401],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009965661,0.0005860682,0.00008575368,0.006546108,0.0001719792,0.0008173397,0.6386954,0.0003422227,0.3517586],"genre_scores_gemma":[0.01507159,0.0007200377,0.00004585254,0.0001013597,0.000281798,1.672314e-7,0.9823437,0.0002071551,0.001228343],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3505303,"threshold_uncertainty_score":0.9999799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06463041015688259,"score_gpt":0.3255393775147998,"score_spread":0.2609089673579172,"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."}}