{"id":"W4383093199","doi":"10.20944/preprints202307.0192.v1","title":"Examining the Potential of Generative Language Models for Aviation Safety Analysis: Case Study and Insights using the Aviation Safety Reporting System (ASRS)","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Fusion (Canada)","funders":"","keywords":"Aviation; Computer science; Aviation safety; Similarity (geometry); Crew; Process (computing); Aviation accident; Risk analysis (engineering); Aeronautics; Artificial intelligence; Engineering; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.008415361,0.001063974,0.0003689873,0.002536983,0.0006475201,0.002341451,0.001156386,0.001231839,0.00134391],"category_scores_gemma":[0.03379125,0.0003886273,0.00116289,0.001601218,0.001228856,0.002893437,0.001668832,0.001648441,0.0005421865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166838,"about_ca_system_score_gemma":0.001161712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006973648,"about_ca_topic_score_gemma":0.009554615,"domain_scores_codex":[0.9913576,0.007004127,0.0002106586,0.0006858783,0.0005712332,0.0001703892],"domain_scores_gemma":[0.9344039,0.05927207,0.001544292,0.00288155,0.001495809,0.0004023344],"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":[0.001822972,0.001544517,0.1081156,0.00248778,0.0006388773,0.00421191,0.03909491,0.4160134,0.02271305,0.05658799,0.01343264,0.3333364],"study_design_scores_gemma":[0.00008378813,0.0004606588,0.009276548,0.0002286751,0.000133707,0.0008688992,0.005906987,0.9363088,0.007788851,0.02442482,0.01441601,0.0001022379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6478656,0.001147807,0.3366525,0.003427844,0.0001355551,0.0005074199,0.00219291,0.002151872,0.005918401],"genre_scores_gemma":[0.8635597,0.0003240315,0.1319577,0.0002446797,0.00005924809,0.0002030882,0.002339071,0.0002337388,0.001078661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008415361,"threshold_uncertainty_score":0.04450518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2380561019543551,"score_gpt":0.3699562733530729,"score_spread":0.1319001713987177,"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."}}