{"id":"W7097075283","doi":"","title":"CAPITAL STOCK AND UNEMPLOYMENT IN CANADA By","year":2010,"lang":"en","type":"article","venue":"","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"NAIRU; Unemployment; Stock (firearms); Generosity; Empirical evidence; Full employment; Capital (architecture); Variable (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":[0.0004710084,0.0001285082,0.00020718,0.001533273,0.001480758,0.00150613,0.0003923377,0.0002237703,0.00368055],"category_scores_gemma":[0.002041675,0.0001010387,0.0002495735,0.002128302,0.0003819087,0.0002584535,0.0005554185,0.0003883561,0.0001880233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02236507,"about_ca_system_score_gemma":0.01541987,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9898474,"about_ca_topic_score_gemma":0.9912224,"domain_scores_codex":[0.9997317,0.00001358396,0.00001516719,0.00003366709,0.0001031579,0.0001027134],"domain_scores_gemma":[0.9986033,0.000103587,0.0002997277,0.00003529741,0.0006764827,0.0002816309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000137395,0.00002459155,0.9799405,0.00005281579,0.0000716412,0.000117161,0.001001001,0.001272219,0.0002535922,0.003503815,0.002383959,0.01124143],"study_design_scores_gemma":[0.000004926545,0.0000081355,0.9921265,0.00003651388,0.0000190042,0.00004349193,0.0006745802,0.001480815,0.0001651975,0.00023805,0.005191174,0.00001163123],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843573,0.001246266,0.0001426773,0.0007667676,0.00001478986,0.00001235216,0.005480803,0.00001234999,0.007966763],"genre_scores_gemma":[0.9968349,0.0001990881,0.00006783154,0.00002005301,0.000003592188,0.000002230304,0.0009932147,0.000002332642,0.001876683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02236507,"threshold_uncertainty_score":0.1622707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549885551011233,"score_gpt":0.1823699819554447,"score_spread":0.1568711264453323,"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."}}