{"id":"W6889011915","doi":"10.25318/2110011401-eng","title":"Advertising and related services, operating expenses, by North American Industry Classification System (NAICS), inactive","year":2020,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Media industry; Advertising research; Advertising campaign; Data collection","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":[0.0008409454,0.001690732,0.001244419,0.006764018,0.0007906872,0.002973849,0.001649752,0.0009707382,0.04741473],"category_scores_gemma":[0.007491972,0.0006219587,0.0009318303,0.01707452,0.000400179,0.001605967,0.00102443,0.001822676,0.07449284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003114634,"about_ca_system_score_gemma":0.005584788,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1833097,"about_ca_topic_score_gemma":0.1963832,"domain_scores_codex":[0.998486,0.0001210816,0.0002402974,0.0003517076,0.000517675,0.0002833102],"domain_scores_gemma":[0.995483,0.0006264287,0.0005440367,0.000532696,0.002444221,0.000369609],"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":[0.00002382596,0.00001329205,0.001818242,0.000176654,0.00001267552,0.000008922739,0.00001184459,0.0001404785,0.0000243698,0.0003419215,0.9958618,0.001565939],"study_design_scores_gemma":[0.00009957269,0.00001522463,0.02042797,0.0002808717,0.00002514909,0.00004446911,0.0001761275,0.0004378425,0.0001967614,0.0005955669,0.97767,0.00003056092],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001564386,0.00003259398,0.00001804509,0.00003371784,0.00002050523,0.000005906132,0.9989401,0.00005468135,0.0007379364],"genre_scores_gemma":[0.0004976441,0.00006141739,0.00007197422,0.00002705474,0.00000955277,0.00003677311,0.9982528,0.00002357961,0.001019075],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8166903,"threshold_uncertainty_score":0.3644854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007663934147245734,"score_gpt":0.2291730812463065,"score_spread":0.2215091470990608,"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."}}