{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007411407,0.0002960058,0.0003308066,0.0000814814,0.000219462,0.0004922844,0.0005561209,0.000162647,0.000002573143],"category_scores_gemma":[0.0001010477,0.0003380979,0.00001148095,0.0004412102,0.00005962548,0.0005820581,0.000216027,0.0005410368,0.000006571991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000648085,"about_ca_system_score_gemma":0.0005516159,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1130708,"about_ca_topic_score_gemma":0.2483927,"domain_scores_codex":[0.9981436,0.0000782951,0.0004903136,0.0006513949,0.0003831976,0.0002532437],"domain_scores_gemma":[0.9983245,0.0002010512,0.0006317559,0.0004345585,0.0001625676,0.0002456145],"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":[0.000002072729,0.00001124687,0.00008005295,0.000268665,0.00002702269,0.00004146471,0.0000776431,0.00000289936,0.000008246036,0.0006805253,0.9822634,0.01653671],"study_design_scores_gemma":[0.0006245461,0.0003023292,0.03891964,0.001272082,0.0002262416,0.0001555224,0.006789621,0.05681563,0.0001970748,0.00006857673,0.8919805,0.002648186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004198994,0.00006659958,0.006539209,0.0001253305,0.0002788111,0.0002481205,0.9921719,0.00004798611,0.0001021758],"genre_scores_gemma":[0.04077213,0.00003792655,0.001503854,0.0001753072,0.00003139631,0.00003013223,0.9574098,0.00001649283,0.00002294089],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1353218,"threshold_uncertainty_score":0.9999071,"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."}}