{"id":"W6901628108","doi":"10.6068/dp14ba8dc1c9429","title":"Most Recent Data (2005). Statistics Canada. CANSIM: Science and Technology - Innovation | Country: Canada | Table: Survey of innovation, logging and manufacturing industries, percentage of plants with expenditures on new machinery or equipment that were supplied from different locations | Variable: From the rest of Canada, Veneer, plywood and engineered wood product manufacturing, 25% to 49% of expenditures, Innovative plants | Units: %, 2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-181.","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; Product (mathematics); Publication; Official statistics; Summary statistics; Statistical analysis; Logging; Business statistics","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":[0.002304986,0.002567177,0.003033813,0.01015624,0.003680175,0.005220074,0.005148032,0.001633149,0.08660189],"category_scores_gemma":[0.0206969,0.001804581,0.002096828,0.05779466,0.0007386383,0.002487846,0.002225168,0.003268955,0.0485973],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06681118,"about_ca_system_score_gemma":0.1656859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9956677,"about_ca_topic_score_gemma":0.9935313,"domain_scores_codex":[0.9939556,0.0003171688,0.000699532,0.0006203578,0.003055195,0.001352199],"domain_scores_gemma":[0.9508991,0.001762643,0.001441547,0.001036566,0.04289176,0.001968433],"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":[0.00002117826,0.000008886885,0.001019194,0.0002641146,0.00001814142,0.000006273233,0.00001962654,0.0001039979,0.0000074124,0.0002782852,0.9969622,0.0012906],"study_design_scores_gemma":[0.000187314,0.0000167139,0.0357337,0.0008817488,0.00009309789,0.00002616587,0.0006602315,0.0003891681,0.0001873145,0.0005244448,0.961206,0.00009416359],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005598307,0.00005152289,0.00001495246,0.0001153638,0.00002252464,0.00001256089,0.9989271,0.00003905964,0.0007609004],"genre_scores_gemma":[0.00090752,0.0003171765,0.00031066,0.0001741096,0.00001779819,0.0001088774,0.9938803,0.00007353418,0.00421012],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9331888,"threshold_uncertainty_score":0.4847513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03697455549323712,"score_gpt":0.2540402418927893,"score_spread":0.2170656863995522,"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."}}