{"id":"W3096268605","doi":"10.3138/jsp.52.1.03","title":"An Analysis of Spam from Predatory Publications in Library and Information Science","year":2020,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Legitimacy; Library science; Computer science; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001578592,0.0000644192,0.0001843862,0.0019824,0.00009760828,0.02500385,0.001615426,0.0000729399,0.00001252152],"category_scores_gemma":[0.001887467,0.00005838351,0.00005199822,0.00590669,0.00005243638,0.6491556,0.0002139831,0.0005960717,5.100981e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003115614,"about_ca_system_score_gemma":0.0003133068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004793711,"about_ca_topic_score_gemma":0.000003123992,"domain_scores_codex":[0.9984292,0.00008422004,0.0005372016,0.0001523004,0.0006813673,0.0001156757],"domain_scores_gemma":[0.9983198,0.0001011606,0.0005988017,0.000249751,0.0004752693,0.0002552204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004172841,0.0000967763,0.8676129,0.00001927862,0.0001545558,0.000003761286,0.01465711,0.003041123,0.01526606,0.06061628,0.0004211816,0.03806927],"study_design_scores_gemma":[0.0002569465,0.0001030616,0.8797364,0.00001971584,0.0000414719,0.000003409751,0.0003852168,0.1144349,0.001282561,0.0007589316,0.002900043,0.00007733768],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9297641,0.0001574971,0.0568171,0.01216904,0.0001690033,0.00004438124,0.00001051661,0.00003767154,0.0008307052],"genre_scores_gemma":[0.9790519,0.00004390442,0.01980329,0.001012211,0.00007900458,5.950322e-7,0.000005655858,0.000002441724,0.000001009885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6241517,"threshold_uncertainty_score":0.9760083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838097363728111,"score_gpt":0.2301992201931874,"score_spread":0.2118182465559063,"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."}}