{"id":"W4407567707","doi":"10.1038/s41598-025-88709-7","title":"Citation manipulation through citation mills and pre-print servers","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University Abu Dhabi","keywords":"Citation; Computer science; Server; Information retrieval; World Wide Web; Data science","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02100642,0.0009435217,0.001453096,0.01437613,0.002783935,0.0119694,0.002566023,0.002030467,0.008820008],"category_scores_gemma":[0.1782275,0.0007742292,0.0007843778,0.03027749,0.002144559,0.01016123,0.003916224,0.002336479,0.006298924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00261164,"about_ca_system_score_gemma":0.002203713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003717962,"about_ca_topic_score_gemma":0.00459932,"domain_scores_codex":[0.9665081,0.009385122,0.003138326,0.00328374,0.01605329,0.001631462],"domain_scores_gemma":[0.7640987,0.1167755,0.05203354,0.0420417,0.02215063,0.002899927],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001008662,0.0005317138,0.4974537,0.001681753,0.0006741046,0.0007261708,0.005097688,0.003989566,0.004850342,0.05293615,0.07009596,0.3609542],"study_design_scores_gemma":[0.0002589363,0.0005605615,0.442892,0.001256479,0.000822551,0.002794777,0.004204156,0.05827054,0.03576601,0.1350968,0.3176335,0.000443623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8142974,0.00786043,0.03578969,0.009042964,0.0009058543,0.000643253,0.01990407,0.005642937,0.1059134],"genre_scores_gemma":[0.9592271,0.00164719,0.01383596,0.0009401827,0.0009506741,0.0002715639,0.008039359,0.0006931865,0.01439482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9979695,"threshold_uncertainty_score":0.1110939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4510459083690589,"score_gpt":0.543189825935932,"score_spread":0.09214391756687312,"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."}}