{"id":"W4393200490","doi":"10.32384/jeahil20604","title":"Brief Communication – concerning algorithmic indexing in MEDLINE","year":2024,"lang":"en","type":"article","venue":"Journal of EAHIL","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; Université de Montréal","funders":"","keywords":"Search engine indexing; MEDLINE; Computer science; Information retrieval; Data science; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.001024361,0.0000672191,0.0001382216,0.0002257595,0.00004119558,0.0002129734,0.0008110282,0.0000446366,0.00002117189],"category_scores_gemma":[0.0001675907,0.0000586259,0.00006104742,0.0004035325,0.00004928575,0.000846607,0.0001347932,0.0004391898,0.00002256422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009914768,"about_ca_system_score_gemma":0.0001412912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004422017,"about_ca_topic_score_gemma":0.00002703834,"domain_scores_codex":[0.9989392,0.00008802482,0.0004838922,0.0001018592,0.0002548498,0.0001321997],"domain_scores_gemma":[0.9991463,0.000305596,0.0001610503,0.0002242273,0.0001152124,0.00004762421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001282498,0.00008105594,0.002965532,0.00003547836,0.00005281567,0.0004834603,0.01455687,0.007096689,0.003790366,0.06336784,0.002245154,0.9053119],"study_design_scores_gemma":[0.0001982551,0.0001821942,0.001839681,0.001327842,0.00001265912,0.0005392952,0.0006380202,0.8840966,0.01361477,0.05083749,0.04645132,0.0002619055],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1359601,0.01029734,0.841023,0.008264765,0.001424681,0.00009209936,6.806301e-7,0.00007924663,0.002858083],"genre_scores_gemma":[0.9680706,0.0002295719,0.03123694,0.0001675593,0.0001834531,9.042085e-7,2.160584e-7,0.000006556913,0.0001042632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.90505,"threshold_uncertainty_score":0.2390695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03961256816276703,"score_gpt":0.322039995119935,"score_spread":0.2824274269571679,"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."}}