{"id":"W2885771445","doi":"","title":"LibGuides: Canadian Guide to Uniform Legal Citation (8th ed): Automatic Citation Generator","year":2016,"lang":"en","type":"libguides","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Citation; Generator (circuit theory); Computer science; Data science; History; Library science; Power (physics); Physics","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":["insufficient_payload"],"category_scores_codex":[0.002024743,0.000486505,0.0005194593,0.001042636,0.001347821,0.0008201167,0.001159718,0.0008260683,0.007976544],"category_scores_gemma":[0.003812181,0.0004321235,0.0002052685,0.001021427,0.0004125863,0.001029596,0.00007499032,0.0002990071,0.005506079],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002819911,"about_ca_system_score_gemma":0.007524123,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7609401,"about_ca_topic_score_gemma":0.9507564,"domain_scores_codex":[0.9950396,0.0003662815,0.00122343,0.0007574496,0.001349948,0.001263246],"domain_scores_gemma":[0.9960752,0.0005983929,0.000378908,0.0005937841,0.001155212,0.001198507],"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":[0.0000028104,0.00001074591,0.00003572672,0.00002599655,0.00003606891,0.00001365276,0.0034031,0.00002367946,0.00003351461,0.1437553,0.8170859,0.03557343],"study_design_scores_gemma":[0.00004508344,0.00008110234,0.00003287556,0.0002567772,0.0000423562,0.00000119582,0.004482009,0.0002652933,0.0003165973,0.004957223,0.9888634,0.0006560885],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001841947,0.0001815048,0.007477261,0.02715163,0.008205757,0.001922147,0.0003407779,0.0006990572,0.9521799],"genre_scores_gemma":[0.03121845,0.0006628853,0.03781279,0.01659904,0.01395568,0.0004912577,0.0005793402,0.0003218752,0.8983587],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1898163,"threshold_uncertainty_score":0.9999523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03992436980095376,"score_gpt":0.3446452356732625,"score_spread":0.3047208658723087,"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."}}