{"id":"W6958215311","doi":"10.6068/dp14ba8cbaddf91","title":"Most Recent Data (2005). Statistics Canada. CANSIM: Science and Technology - Research and Development | Country: Canada | Table: Survey of innovation, logging and manufacturing industries, percentage of plants with expenditures on research and development services that were supplied from different locations | Variable: From province or territory, Converted paper product manufacturing, 0% of expenditures, Innovative plants | Units: %, 2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-182.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Polymer Science and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Publication; Summary statistics; Product (mathematics); Statistical analysis; National accounts; Logging","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":[0.002428563,0.002535728,0.002915676,0.01003517,0.003935592,0.005407888,0.005224558,0.001731128,0.1042046],"category_scores_gemma":[0.02325438,0.001782735,0.002115658,0.05313947,0.0007426714,0.002716969,0.00233619,0.003387067,0.06210387],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06376195,"about_ca_system_score_gemma":0.1606076,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9944959,"about_ca_topic_score_gemma":0.9923867,"domain_scores_codex":[0.9942169,0.0003143984,0.0006733424,0.0006307515,0.002844686,0.001319874],"domain_scores_gemma":[0.9482489,0.00190777,0.001388627,0.001167557,0.04524614,0.00204106],"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":[0.00001788452,0.000006650359,0.0007828181,0.0002330652,0.0000140511,0.000005605967,0.00001722979,0.00008362419,0.000006951017,0.0002516453,0.9974719,0.001108659],"study_design_scores_gemma":[0.0001778027,0.00001371442,0.02773813,0.0009221984,0.00007623826,0.00002569837,0.0005677489,0.0003244404,0.0001760597,0.0005618453,0.9693298,0.0000863835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004188871,0.00004595352,0.00001425507,0.0001046457,0.00002298654,0.00001232494,0.9989902,0.00003629811,0.0007313435],"genre_scores_gemma":[0.0007354168,0.0002956419,0.0003098377,0.0001737,0.00001959469,0.0001237573,0.994101,0.00008262499,0.004158425],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9362381,"threshold_uncertainty_score":0.4626275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05864869074866581,"score_gpt":0.2834550278688459,"score_spread":0.2248063371201801,"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."}}