{"id":"W6920289942","doi":"10.6068/dp14ba8c4697227","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 that have a source of the license | Variable: Plants that acquired or did not acquire licenses, Paper manufacturing, Canadian federal government lab, All 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; Official statistics; Government (linguistics); License; Summary statistics; Publication; Social 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":[0.00243149,0.002606607,0.002961297,0.01038374,0.004361734,0.005785447,0.005289923,0.001688206,0.1026265],"category_scores_gemma":[0.0237111,0.001799435,0.002067502,0.05603165,0.0007923492,0.002769274,0.002450329,0.003318002,0.06361745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06644253,"about_ca_system_score_gemma":0.1668378,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954183,"about_ca_topic_score_gemma":0.9934497,"domain_scores_codex":[0.9939819,0.0003139811,0.0006479912,0.0006385644,0.003038763,0.001378801],"domain_scores_gemma":[0.9460824,0.001952003,0.001329552,0.00127276,0.04720142,0.002161853],"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":[0.0000164659,0.000006778338,0.0007549865,0.0002035714,0.00001259904,0.000005305558,0.00001855101,0.00007996723,0.000006756614,0.0002554187,0.9974462,0.00119346],"study_design_scores_gemma":[0.0001464012,0.00001182002,0.02478927,0.0007821671,0.00006711813,0.00002102112,0.0005649842,0.0003247378,0.0001681796,0.0005259316,0.972514,0.00008438515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004257918,0.00004094554,0.00001562042,0.0001086853,0.00002243196,0.00001175403,0.9989126,0.00004552119,0.0007999095],"genre_scores_gemma":[0.0007111158,0.0002581391,0.0003368755,0.0001630697,0.0000172675,0.0001110165,0.9941954,0.00008902717,0.004118189],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1026265,"threshold_uncertainty_score":0.4820765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04732158139239961,"score_gpt":0.2542939073045141,"score_spread":0.2069723259121145,"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."}}