{"id":"W3042696781","doi":"10.1001/jamanetworkopen.2020.10911","title":"Leveraging Tweets, Citations, and Social Networks to Improve Bibliometrics","year":2020,"lang":"en","type":"letter","venue":"JAMA Network Open","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bibliometrics; Data science; Information retrieval; Computer science; Altmetrics; Social network analysis; Social media; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["bibliometrics","open_science"],"category_scores_codex":[0.02618768,0.0006347002,0.001433308,0.1413619,0.001408999,0.04577521,0.01078048,0.001235566,0.0005837993],"category_scores_gemma":[0.0225424,0.0005248545,0.000309826,0.5799105,0.0002068066,0.00153822,0.01150126,0.004046767,0.0006638794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002594908,"about_ca_system_score_gemma":0.0004718599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002305512,"about_ca_topic_score_gemma":0.000009629142,"domain_scores_codex":[0.9804852,0.001228893,0.001761583,0.002762346,0.01165715,0.002104823],"domain_scores_gemma":[0.9800791,0.01350982,0.0009440559,0.00123009,0.003441132,0.0007958354],"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":[0.00004043508,0.00001559466,0.005447405,0.00001590247,0.00006573945,0.0002004444,0.0001448266,0.0002686627,0.00000117705,0.00006426802,0.8488158,0.1449198],"study_design_scores_gemma":[0.0006022723,0.0001801641,0.02149158,0.00003885341,0.00003183966,0.000007714544,0.0001288099,0.009898434,0.000001208371,0.004965649,0.9619654,0.0006880378],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001064233,0.002449663,0.02012425,0.9628044,0.003050348,0.002037717,0.0002031903,0.00007349391,0.008192738],"genre_scores_gemma":[0.01880058,0.000872471,0.00579474,0.94527,0.02006411,0.0001868122,0.00028389,0.0001415872,0.008585786],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.4385485,"threshold_uncertainty_score":0.999891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5134223927168502,"score_gpt":0.5263683747941194,"score_spread":0.01294598207726916,"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."}}