{"id":"W6907500219","doi":"10.21415/t5sk6b","title":"HomeBank English Winnipeg Corpus","year":2016,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Subject (documents); Identification (biology); Feature (linguistics); Field (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001046028,0.002403409,0.00130191,0.003853914,0.001662891,0.001853383,0.002696757,0.001475892,0.06642504],"category_scores_gemma":[0.004266607,0.0007982662,0.0009544266,0.004231782,0.0008214998,0.001578443,0.002835471,0.001606738,0.07285198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00216153,"about_ca_system_score_gemma":0.004872032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1848883,"about_ca_topic_score_gemma":0.29869,"domain_scores_codex":[0.9989331,0.0002374855,0.00008786646,0.0003165285,0.0002438122,0.000181242],"domain_scores_gemma":[0.9986733,0.0002935163,0.00005377694,0.0003145641,0.0004779523,0.000186785],"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":[0.0001915037,0.00003726735,0.0003807147,0.0003526786,0.000023705,0.00007470916,0.0001183235,0.0002520706,0.0005448657,0.0007378553,0.988142,0.009144485],"study_design_scores_gemma":[0.0003088648,0.00002751619,0.00650221,0.0001982634,0.00005371213,0.0002332599,0.0003860569,0.001338017,0.001526762,0.001363724,0.9880099,0.00005174735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005641573,0.0007589923,0.001258018,0.0004260081,0.0003125697,0.0001764307,0.9733455,0.004113964,0.01396688],"genre_scores_gemma":[0.003597297,0.0001529037,0.001599864,0.00008512895,0.00002433177,0.0001799054,0.9843164,0.0007416059,0.009302656],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1848883,"threshold_uncertainty_score":0.3676243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0348585073452393,"score_gpt":0.3089339598501531,"score_spread":0.2740754525049138,"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."}}