{"id":"W2004098455","doi":"10.1007/s11192-013-1069-6","title":"Effect of collaboration network structure on knowledge creation and technological performance: the case of biotechnology in Canada","year":2013,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Network structure; Productivity; Quality (philosophy); Production (economics); Knowledge management; Field (mathematics); Network analysis; Biotechnology; Business; Computer science; Data science; Engineering; Biology; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":true,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":true,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01540162,0.000130408,0.0003457652,0.05063199,0.0001753469,0.000285279,0.001128622,0.0001924777,0.00009330339],"category_scores_gemma":[0.03425495,0.00006740615,0.00003072491,0.4314651,0.0005514974,0.000243503,0.0004486114,0.0003088981,0.000007501409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003298815,"about_ca_system_score_gemma":0.0005209459,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05370288,"about_ca_topic_score_gemma":0.07347152,"domain_scores_codex":[0.9953133,0.000356166,0.0006361364,0.0004599351,0.002798569,0.0004358876],"domain_scores_gemma":[0.9895739,0.00773305,0.0003446456,0.0006007021,0.001624832,0.0001228694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002286156,0.00003863651,0.2841277,0.00002394552,0.000005109296,0.00001150405,0.00006510794,0.0009922805,0.0009183518,0.001664325,0.001864912,0.7102652],"study_design_scores_gemma":[0.001534187,0.002950004,0.740337,0.0000499169,0.00001660216,0.0001227433,0.001751652,0.150799,0.09668067,0.00259415,0.002805426,0.0003586424],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970747,0.0008125238,0.00008144907,0.0005073469,0.0003035494,0.0005588941,0.00002506945,0.000007662398,0.0006287817],"genre_scores_gemma":[0.9995803,0.0001863151,0.0001270948,0.00001459261,0.00001331321,0.00001261571,0.000001521015,0.000004052707,0.00006016437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7099066,"threshold_uncertainty_score":0.9738799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1136474693055103,"score_gpt":0.4694544594801149,"score_spread":0.3558069901746047,"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."}}