{"id":"W7070765842","doi":"","title":"Power Co. of Canada (POW) – Investment Analysts’ Weekly Ratings Updates","year":2016,"lang":"en","type":"other","venue":"","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Investment (military); Power (physics); Government (linguistics); Measure (data warehouse); Work (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001995902,0.0008726969,0.0006919519,0.003770422,0.003061986,0.008352677,0.0008534734,0.001522044,0.226974],"category_scores_gemma":[0.01244321,0.0005581338,0.0003318214,0.005109871,0.0007529667,0.00229901,0.0008194076,0.002737593,0.1229666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01036919,"about_ca_system_score_gemma":0.02329452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6227093,"about_ca_topic_score_gemma":0.7772581,"domain_scores_codex":[0.9952708,0.00009757076,0.00009022117,0.000196504,0.004058936,0.0002859475],"domain_scores_gemma":[0.977466,0.0006234322,0.000372681,0.0005447729,0.01964724,0.001346036],"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":[0.00001980485,0.000006924209,0.0002890211,0.00002239009,0.000002079106,0.00001364131,0.00001848286,0.00006287886,0.00008290305,0.000935455,0.9837173,0.01482896],"study_design_scores_gemma":[0.000005265229,0.000005237215,0.001820092,0.00003608007,0.000003089471,0.0000148404,0.00006127064,0.0002179971,0.0002035279,0.0002254329,0.9973987,0.000008467956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002754524,0.003264501,0.001436157,0.01602616,0.005831172,0.0002353773,0.05081379,0.002438398,0.9172],"genre_scores_gemma":[0.01198513,0.002174461,0.001008334,0.0008320585,0.0005518122,0.00004056671,0.01317986,0.0007436695,0.9694842],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3772907,"threshold_uncertainty_score":0.7593036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005881789945159242,"score_gpt":0.2182995623705734,"score_spread":0.2124177724254141,"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."}}