{"id":"W7112739578","doi":"","title":"Analysis of the prediction accuracy of the United States Navy repair turn-around time forecast model","year":2003,"lang":"","type":"dissertation","venue":"Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Navy; Process (computing); Quarter (Canadian coin); Poisson process; Probabilistic forecasting; Current (fluid); Reliability (semiconductor)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts"],"category_scores_codex":[0.006221926,0.00231041,0.002874233,0.002706634,0.005556469,0.0008263437,0.008554835,0.0009980029,0.0002914022],"category_scores_gemma":[0.01783997,0.001331351,0.006166817,0.01691217,0.006056992,0.001373785,0.001786036,0.00504053,0.0003090042],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066374,"about_ca_system_score_gemma":0.006009277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004510132,"about_ca_topic_score_gemma":0.0009038047,"domain_scores_codex":[0.9775117,0.003385444,0.006716356,0.003065047,0.007463722,0.001857729],"domain_scores_gemma":[0.9754522,0.003991046,0.007326906,0.005883658,0.006301484,0.001044737],"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":[0.003391983,0.001959537,0.0174633,0.0004504724,0.009156005,0.0000169541,0.003121572,0.8702265,0.01408746,0.03582411,0.04119956,0.00310251],"study_design_scores_gemma":[0.001529685,0.0004838728,0.04780696,0.00157825,0.007854915,0.0001508187,0.0014452,0.8325219,0.00434739,0.09478898,0.00590911,0.001582896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502089,0.0006624071,0.007940709,0.005489702,0.002369847,0.006610285,0.02301595,0.0003546962,0.003347507],"genre_scores_gemma":[0.9733236,0.001526281,0.002942861,0.001293656,0.0004496241,0.0005242618,0.008914996,0.0002371877,0.01078752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05896488,"threshold_uncertainty_score":0.9996257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05099345830294669,"score_gpt":0.3216437648877484,"score_spread":0.2706503065848017,"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."}}