{"id":"W4408695062","doi":"10.1115/1.4068275","title":"Human–Artificial Intelligence Teaming for Scientific Information Extraction From Data-Driven Additive Manufacturing Literature Using Large Language Models","year":2025,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Research Council Canada; Mitacs; McGill University","keywords":"Computer science; Information extraction; Artificial intelligence; Data extraction; Extraction (chemistry); Natural language processing; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009649859,0.0001113762,0.0001468803,0.001091456,0.0002447575,0.0008339902,0.0002859633,0.00005830019,0.000001503266],"category_scores_gemma":[0.0001418655,0.0001101956,0.00002354248,0.0005003046,0.00003001057,0.01366363,0.00008266043,0.0002329474,3.225983e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000121669,"about_ca_system_score_gemma":0.00005356162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005255288,"about_ca_topic_score_gemma":0.000001375608,"domain_scores_codex":[0.9988244,0.000005760504,0.0006392526,0.00008947835,0.0002375375,0.0002035392],"domain_scores_gemma":[0.999352,0.00006968666,0.0002046544,0.0001303511,0.0002003663,0.00004292069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003323105,0.00000367967,0.000005847115,0.0001215424,0.000004841012,2.814161e-7,0.003801469,0.9628339,0.0004908803,0.0004586247,0.00000720146,0.03226843],"study_design_scores_gemma":[0.0001148035,0.000007828163,0.0002862148,0.0005276743,0.00000793071,0.000005414072,0.001103871,0.986066,0.01130854,0.0002225875,0.0002467248,0.000102425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3615545,0.00005282692,0.6376727,0.000006764625,0.0005344371,0.00007559452,0.00002355373,0.00003000971,0.00004961801],"genre_scores_gemma":[0.9727024,0.00002022004,0.02713597,0.00001425937,0.00006756431,9.930629e-7,0.00005377751,0.000004021037,7.559273e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6111479,"threshold_uncertainty_score":0.99058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742863816281132,"score_gpt":0.2845741054651874,"score_spread":0.2671454673023761,"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."}}