{"id":"W4322631583","doi":"10.31234/osf.io/j7mt4","title":"How to build up big team science: A practical guide for large-scale collaborations","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Manitoba; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; John Templeton Foundation","keywords":"Multidisciplinary approach; Set (abstract data type); Corporate governance; Scale (ratio); Engineering ethics; Big data; Knowledge management; Data science; Computer science; Engineering management; Engineering; Political science; Business","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08418912,0.004120588,0.002404447,0.007545931,0.008482752,0.02531662,0.008809285,0.01005619,0.03119947],"category_scores_gemma":[0.1322384,0.004319927,0.002614362,0.006238554,0.01542809,0.02250145,0.01724746,0.01976151,0.04679232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005434691,"about_ca_system_score_gemma":0.02342688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004032686,"about_ca_topic_score_gemma":0.009895092,"domain_scores_codex":[0.9178039,0.05756677,0.007116351,0.0037742,0.01218973,0.001548832],"domain_scores_gemma":[0.8563265,0.08035272,0.006278403,0.02184422,0.02030564,0.01489265],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006396822,0.0002731888,0.0007270541,0.002278235,0.0001057422,0.001181615,0.02118129,0.003259192,0.00180989,0.2041013,0.5363352,0.2286832],"study_design_scores_gemma":[0.00005993572,0.0000508727,0.0001829287,0.001412281,0.00001124522,0.0004804421,0.004062462,0.001491162,0.0003400071,0.209485,0.782338,0.00008571487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000703679,0.003678157,0.8646858,0.08143599,0.003713987,0.004399714,0.001084145,0.007713778,0.03258471],"genre_scores_gemma":[0.002635165,0.001920378,0.9795544,0.003757655,0.000434936,0.003757915,0.0003768603,0.001317113,0.006245563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9158109,"threshold_uncertainty_score":0.4452399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.218109984688439,"score_gpt":0.5111105963022756,"score_spread":0.2930006116138366,"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."}}