{"id":"W2088858100","doi":"10.1515/itit-2014-1048","title":"Tweets vs. Mendeley readers: How do these two social media metrics differ?","year":2014,"lang":"en","type":"article","venue":"it - Information Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Altmetrics; Microblogging; Social media; Citation; Computer science; Set (abstract data type); World Wide Web; Measure (data warehouse); Information retrieval; Data science; Data mining","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","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01270642,0.0002356348,0.0004876581,0.1058998,0.0003699497,0.002628952,0.003416688,0.0005233515,0.0004005412],"category_scores_gemma":[0.09312037,0.0001707421,0.0001530289,0.1673661,0.0004514356,0.00258036,0.0009446944,0.0006209579,0.001893987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001987116,"about_ca_system_score_gemma":0.000123725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001265568,"about_ca_topic_score_gemma":0.00001508849,"domain_scores_codex":[0.9908238,0.0001793693,0.0009830272,0.0004395197,0.006722489,0.0008518156],"domain_scores_gemma":[0.9914044,0.003801541,0.0006614548,0.0009433558,0.002941094,0.0002481631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000185399,0.00004942386,0.004147953,0.000007806936,0.00001847352,0.000001814453,0.001873569,0.00000708856,0.00008324618,0.04040992,0.1083021,0.84508],"study_design_scores_gemma":[0.001706028,0.0002563061,0.008644419,0.000008825069,0.0000132568,0.00002285586,0.009605545,0.01090421,0.003624406,0.1042996,0.8604378,0.0004768678],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.482155,0.0004878572,0.2913538,0.1493312,0.003656607,0.00123922,0.0002312799,0.001240131,0.07030489],"genre_scores_gemma":[0.9969621,0.0001178877,0.001781153,0.0006997414,0.0001261177,0.00003310718,0.00002508472,0.00001061153,0.0002441764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8446032,"threshold_uncertainty_score":0.9988831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3657388384707008,"score_gpt":0.4990475180976115,"score_spread":0.1333086796269107,"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."}}