{"id":"W6939183682","doi":"10.6068/dp14ba8ae7d5b86","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 the United States, Primary metal 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":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Summary statistics; Publication; Statistical analysis; National accounts; Statistical survey; 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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002376532,0.002543079,0.002928617,0.009856164,0.003850276,0.005616123,0.005074762,0.001707835,0.1059369],"category_scores_gemma":[0.02277828,0.001824123,0.002049459,0.05445915,0.0007180274,0.002740555,0.002314332,0.003403466,0.06591928],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05883845,"about_ca_system_score_gemma":0.1533452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936745,"about_ca_topic_score_gemma":0.9911267,"domain_scores_codex":[0.9942583,0.000321568,0.0006562879,0.0006159942,0.002832967,0.001314933],"domain_scores_gemma":[0.9492664,0.001948346,0.001352755,0.001171396,0.04426335,0.001997828],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000173208,0.000006753438,0.0007470516,0.000217182,0.00001335845,0.000005510194,0.0000164863,0.00008402607,0.000006574247,0.0002606346,0.9975072,0.001118038],"study_design_scores_gemma":[0.0001650467,0.00001286574,0.02464833,0.0008479488,0.00006935652,0.00002422696,0.0005459029,0.0003181419,0.0001717275,0.0005725759,0.9725404,0.00008356003],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004016217,0.00004437161,0.0000146889,0.0001019769,0.00002259254,0.0000114005,0.9989632,0.00003980556,0.0007618022],"genre_scores_gemma":[0.0006935445,0.0002869027,0.0003157046,0.0001681961,0.00001895111,0.0001107011,0.9942146,0.00008895963,0.004102453],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9976234,"threshold_uncertainty_score":0.4269049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07888899715317305,"score_gpt":0.3005532573420602,"score_spread":0.2216642601888871,"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."}}