{"id":"W2601959818","doi":"","title":"Official Documents- Amendment No. 3 to the Administration Arrangement with the Government of Canada for MDTF Grant TF071187","year":2016,"lang":"en","type":"article","venue":"","topic":"Legal case studies and regulations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Political science; Administration (probate law); Public administration; Library science; Law; Computer science","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":[0.01091554,0.0007438712,0.001183312,0.003970178,0.01203943,0.009833035,0.005170316,0.006966764,0.1221973],"category_scores_gemma":[0.03796237,0.001122967,0.001002617,0.004524178,0.002507998,0.001942349,0.001948978,0.005751449,0.0463901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04214566,"about_ca_system_score_gemma":0.2372861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9463211,"about_ca_topic_score_gemma":0.9711954,"domain_scores_codex":[0.9712358,0.001172441,0.0009718216,0.001073877,0.02108903,0.00445697],"domain_scores_gemma":[0.9427504,0.005401189,0.0009634594,0.003715854,0.04288379,0.004285228],"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":[0.0001803467,0.0001541566,0.0009840301,0.0001067322,0.0000155198,0.00009759846,0.000967226,0.0001284171,0.0009346841,0.04354525,0.940651,0.01223508],"study_design_scores_gemma":[0.00008333877,0.00005514859,0.008829073,0.0001852847,0.0000314473,0.00005308921,0.0008963053,0.0002597616,0.0009448088,0.002495576,0.9861029,0.00006326206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006945425,0.001143958,0.002348598,0.0314238,0.003194937,0.002976005,0.05770672,0.001208409,0.8930521],"genre_scores_gemma":[0.0178327,0.0005687988,0.003868232,0.005556385,0.0002614076,0.0004757537,0.01266396,0.0002719459,0.9585009],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1221973,"threshold_uncertainty_score":0.4087905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171598475832306,"score_gpt":0.2558109153137961,"score_spread":0.244094930555473,"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."}}