{"id":"W4405127663","doi":"10.1007/s41060-024-00693-9","title":"AI-generated or AI touch-up? Identifying AI contribution in text data","year":2024,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Data science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01400192,0.00079272,0.0009360844,0.01001447,0.002142297,0.008445439,0.001913635,0.002304328,0.004941438],"category_scores_gemma":[0.1680451,0.0006056066,0.0007207776,0.01070006,0.002681803,0.01485793,0.006041397,0.003072195,0.002012529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001475832,"about_ca_system_score_gemma":0.001985791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002525406,"about_ca_topic_score_gemma":0.003010006,"domain_scores_codex":[0.9839977,0.008135396,0.001067052,0.002197895,0.003917029,0.0006848919],"domain_scores_gemma":[0.8047562,0.1559989,0.008271168,0.01199719,0.01529195,0.003684607],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001791427,0.0006115535,0.2581332,0.002611614,0.000752529,0.001806261,0.04472252,0.006126416,0.01225432,0.09306535,0.03091031,0.5472145],"study_design_scores_gemma":[0.0002308968,0.0005373706,0.1610576,0.001909964,0.001286557,0.003022018,0.04326016,0.2398813,0.01762825,0.3585889,0.1723049,0.0002921338],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6913803,0.01088501,0.2123412,0.01530167,0.001921841,0.0006355016,0.004330489,0.002930136,0.06027382],"genre_scores_gemma":[0.9549793,0.00116555,0.03401045,0.0008398809,0.0008884791,0.0002740227,0.002854231,0.0005764861,0.00441166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9859981,"threshold_uncertainty_score":0.07405013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1328814845314103,"score_gpt":0.4163926192477443,"score_spread":0.283511134716334,"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."}}