{"id":"W4252188144","doi":"10.1163/2210-7975_hrd-1714-0119","title":"submission-to-the-canadian-government-in-preparation-for-the-50th-session-of-the-unchr-from-the-interchurch-committee-on-human-rights-in-latin-america-jan-1994-12-pp","year":2016,"lang":"en","type":"dataset","venue":"Human Rights Documents online","topic":"Legal Education and Practice Innovations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Latin Americans; Political science; Government (linguistics); Library science; Public administration; Law; Business; Computer science; Advertising; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001686936,0.0006113524,0.0006138544,0.0002887037,0.006086911,0.000653861,0.003464424,0.0004339797,0.006915229],"category_scores_gemma":[0.0007477238,0.000301529,0.0002872124,0.0009348707,0.0007839694,0.0006423339,0.0002542673,0.001500548,0.0003587477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688905,"about_ca_system_score_gemma":0.001374329,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4813322,"about_ca_topic_score_gemma":0.9368609,"domain_scores_codex":[0.9936572,0.00101231,0.001519328,0.0008672255,0.002043515,0.000900408],"domain_scores_gemma":[0.9929212,0.003070334,0.001269419,0.002118242,0.0002712852,0.0003494589],"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":[0.00005210593,0.0004498047,0.0002025067,0.00001379318,0.000100252,0.000004019087,0.002252743,0.00001341518,0.000009236238,0.01520549,0.9812777,0.0004189571],"study_design_scores_gemma":[0.0007398934,0.00009660368,0.001623276,0.0005034458,0.0001054398,7.296517e-7,0.000358488,0.00001021943,0.00002338174,0.01018823,0.9858959,0.0004543976],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01098639,0.0002000831,0.00004102703,0.1231373,0.006790734,0.00724717,0.8377604,0.0000745546,0.01376227],"genre_scores_gemma":[0.2076738,0.0002460703,0.0007225932,0.02166879,0.01015155,0.002580797,0.5998328,0.0002197207,0.1569038],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4555287,"threshold_uncertainty_score":0.9999437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05825050803196535,"score_gpt":0.4165524129577917,"score_spread":0.3583019049258264,"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."}}