{"id":"W4226283917","doi":"10.1093/gigascience/giab095","title":"Citation needed? Wikipedia bibliometrics during the first wave of the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"GigaScience","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Bibliometrics; Coronavirus disease 2019 (COVID-19); Citation; Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Information retrieval; Computer science; Library science; Biology; Medicine; Virology; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.003003742,0.0002919919,0.0007689767,0.04258605,0.001047182,0.004600259,0.0006058014,0.0008506309,0.002628857],"category_scores_gemma":[0.05802827,0.0001839494,0.000458797,0.05477741,0.0008239881,0.00451375,0.00197161,0.0005751988,0.0009124886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001626997,"about_ca_system_score_gemma":0.001228744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01195019,"about_ca_topic_score_gemma":0.01194329,"domain_scores_codex":[0.9960009,0.000848768,0.0008657891,0.0006877329,0.001277942,0.0003188589],"domain_scores_gemma":[0.9219646,0.03440384,0.02621301,0.002405962,0.01266961,0.002343013],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004112507,0.000083202,0.8239338,0.003360066,0.0004789932,0.0006659713,0.01351549,0.001437398,0.001755159,0.009504035,0.04452063,0.1003341],"study_design_scores_gemma":[0.00001676874,0.00004770388,0.9256862,0.0007511881,0.0001885648,0.0008850112,0.00957133,0.00275102,0.0009915489,0.003258031,0.05576436,0.00008833943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131975,0.01340359,0.0009117873,0.004226347,0.0003105325,0.00006180835,0.04693591,0.0002499307,0.02070248],"genre_scores_gemma":[0.972353,0.003939144,0.001224728,0.0002151703,0.000477847,0.00007717794,0.0196463,0.0001026192,0.001964022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9969963,"threshold_uncertainty_score":0.02376127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.086409079141209,"score_gpt":0.3519599574380511,"score_spread":0.2655508782968422,"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."}}