{"id":"W2952166899","doi":"","title":"Mapping of Research Productivity on Nanotechnology in Canada: A Scientometric Profile","year":2019,"lang":"en","type":"article","venue":"Lincoln (University of Nebraska)","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; Scientometrics; Geography; Bibliometrics; Regional science; Data science; Nanotechnology; Library science; Computer science; Economics; Economic growth; Materials science","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":[0.002765934,0.00035545,0.00067107,0.04110517,0.003617795,0.00497663,0.0008464786,0.0003999367,0.002327554],"category_scores_gemma":[0.01426132,0.0001721786,0.0007899295,0.09997143,0.0008733914,0.001324751,0.001717955,0.0003809104,0.0006796592],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03809869,"about_ca_system_score_gemma":0.07893531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.964775,"about_ca_topic_score_gemma":0.9642997,"domain_scores_codex":[0.994702,0.0001726148,0.0002873211,0.0002988194,0.003883334,0.0006558652],"domain_scores_gemma":[0.9788482,0.001870617,0.002069452,0.0003293805,0.0154043,0.001477944],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001735872,0.00005871043,0.8272192,0.001061846,0.0002238515,0.0004212901,0.005805802,0.002254726,0.001799134,0.005539211,0.01409095,0.1413517],"study_design_scores_gemma":[0.000004270685,0.00003245484,0.9661739,0.0001496463,0.00007014057,0.0001475138,0.004493715,0.00201483,0.0007863156,0.0003209593,0.02577143,0.00003481322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023462,0.006886479,0.001308837,0.002522373,0.0000531866,0.0001561864,0.03696563,0.0002225402,0.04953867],"genre_scores_gemma":[0.9761475,0.00538417,0.001488435,0.0001140065,0.00003267689,0.00005746623,0.009182018,0.00003721319,0.007556402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.997234,"threshold_uncertainty_score":0.2764266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4355775233680524,"score_gpt":0.4775579212335161,"score_spread":0.0419803978654637,"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."}}