{"id":"W6901797071","doi":"10.6068/dp14ba8f5803055","title":"Most Recent Data (2005). Statistics Canada. CANSIM: Information and Communications Technology - Information and Communications Technology Sector | Country: Canada | Table: Survey of innovation, logging and manufacturing industries, percentage of total revenue from the sale of products to selected industries | Variable: Sale of products to forestry and logging industry, Wood product manufacturing, 50% to 74% of revenues, Innovative plants | Units: %, 2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-127.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Official statistics; Revenue; Census; Product (mathematics); Summary statistics; Descriptive statistics; Logging; Information and Communications Technology","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":[0.002452412,0.002492507,0.002744878,0.009605768,0.004109779,0.005168562,0.004961487,0.00164318,0.09591512],"category_scores_gemma":[0.02204579,0.001662955,0.002025734,0.04885026,0.0007051986,0.002472606,0.002242579,0.003172595,0.05833882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06316802,"about_ca_system_score_gemma":0.1572428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954187,"about_ca_topic_score_gemma":0.9938354,"domain_scores_codex":[0.9942037,0.0003179431,0.0006213067,0.000619332,0.002962967,0.001274783],"domain_scores_gemma":[0.9523454,0.001670855,0.001204123,0.001084212,0.04179212,0.001903367],"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":[0.00001647473,0.000006382963,0.0007627507,0.0002184923,0.00001325731,0.000005658213,0.00001779489,0.00008472476,0.00000721161,0.0002638203,0.997314,0.00128931],"study_design_scores_gemma":[0.0001364411,0.00001206177,0.02565434,0.0008063255,0.00006955012,0.00002258771,0.0005373809,0.0003284314,0.0001530452,0.0004996865,0.9716984,0.00008179413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004873139,0.00005370129,0.00001624627,0.0001245967,0.00002543935,0.00001234081,0.9988046,0.00004076956,0.0008736029],"genre_scores_gemma":[0.000820749,0.0003182424,0.0003557166,0.0001935237,0.00001913948,0.0001104517,0.9934442,0.00008164175,0.004656299],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09591512,"threshold_uncertainty_score":0.4583182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03701969750244383,"score_gpt":0.2571977002444107,"score_spread":0.2201780027419669,"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."}}