{"id":"W4387245955","doi":"10.5195/jmla.2023.1826","title":"Introducing the Journal of the Medical Library Association’s policy on the use of generative artificial intelligence in submissions","year":2023,"lang":"en","type":"editorial","venue":"Journal of the Medical Library Association JMLA","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Generative grammar; Association (psychology); Medical library; Computer science; Data science; Library science; Artificial intelligence; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05008714,0.0016771,0.002195329,0.006080697,0.009041207,0.02332158,0.004250454,0.03429969,0.01455299],"category_scores_gemma":[0.1860821,0.001458472,0.002724333,0.00274544,0.008694958,0.00980663,0.003644381,0.03877478,0.01529698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009891262,"about_ca_system_score_gemma":0.02258777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004253539,"about_ca_topic_score_gemma":0.009861769,"domain_scores_codex":[0.9497545,0.008882665,0.007899666,0.002494042,0.02780018,0.003168955],"domain_scores_gemma":[0.7527983,0.113932,0.01545603,0.007692101,0.0844994,0.02562211],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001710294,0.00002012624,0.00004178845,0.0001667256,0.000006963189,0.0001275809,0.0000797103,0.00002900546,0.0000739769,0.002265105,0.9917234,0.005448675],"study_design_scores_gemma":[0.00002819629,0.00001693466,0.0001339281,0.0004973119,0.00001296756,0.0001282795,0.00008961906,0.00009387484,0.0001291577,0.002004932,0.9968351,0.00002982026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00008989847,0.002112803,0.000483552,0.2476008,0.7439432,0.00008276127,0.00006115448,0.0002141221,0.005411713],"genre_scores_gemma":[0.001563368,0.002857978,0.001357422,0.2147925,0.7572734,0.0001418969,0.00005508812,0.0002717396,0.02168664],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.9499128,"threshold_uncertainty_score":0.2648893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09722635263467401,"score_gpt":0.3837270670926456,"score_spread":0.2865007144579716,"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."}}