{"id":"W3172395812","doi":"10.3390/su13126664","title":"Knowledge Sharing in R&D Teams: An Evolutionary Game Model","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Knowledge Management and Sharing","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Social Science Foundation of Jiangsu Province; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Knowledge sharing; Complementarity (molecular biology); Knowledge management; Computer science; Mechanism (biology); Evolutionary game theory; Game theory; Business; Microeconomics; Economics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001857223,0.0007052795,0.0007768284,0.0009026303,0.001307346,0.002434429,0.001870103,0.002333449,0.004505381],"category_scores_gemma":[0.004486966,0.0003406483,0.0009263715,0.0008597784,0.001506324,0.003183511,0.001617312,0.0009518535,0.0003656182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146605,"about_ca_system_score_gemma":0.00191378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009178226,"about_ca_topic_score_gemma":0.004905073,"domain_scores_codex":[0.9985228,0.0008281522,0.00005169518,0.0002042483,0.000180063,0.0002130584],"domain_scores_gemma":[0.998143,0.001162846,0.0002602573,0.00007318994,0.0001493959,0.0002112933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001813713,0.0003500255,0.006268305,0.0001110521,0.0001329839,0.0009603204,0.001741614,0.6530619,0.00233389,0.3169153,0.001028376,0.01691492],"study_design_scores_gemma":[0.00007539666,0.0001144064,0.0009483136,0.00001768923,0.00004516189,0.0001031843,0.0004312346,0.9551005,0.0001881948,0.04140408,0.001544739,0.00002709288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.527805,0.0003848441,0.394109,0.002740856,0.00007119372,0.0004737317,0.0002330885,0.00006803,0.07411421],"genre_scores_gemma":[0.9751947,0.0002485093,0.01712399,0.00008947812,0.00001368261,0.0002555776,0.00003696359,0.000005355887,0.007031804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009178226,"threshold_uncertainty_score":0.01824957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04378147323848128,"score_gpt":0.3710070330646321,"score_spread":0.3272255598261508,"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."}}