{"id":"W3130185535","doi":"10.1109/icmla51294.2020.00123","title":"Forecasting Attrition from the Canadian Armed Forces using Multivariate LSTM","year":2020,"lang":"en","type":"article","venue":"","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence","funders":"","keywords":"Univariate; Multivariate statistics; Attrition; Computer science; Volume (thermodynamics); Artificial intelligence; Multivariate analysis; Econometrics; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001895108,0.0008128637,0.0005258521,0.001388507,0.0009134772,0.0008880939,0.001452008,0.0006375047,0.00238085],"category_scores_gemma":[0.007938021,0.0002940182,0.0005957879,0.002055287,0.0003224137,0.0005638161,0.0005998531,0.00153445,0.0006530655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009921601,"about_ca_system_score_gemma":0.008336768,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9501133,"about_ca_topic_score_gemma":0.9388615,"domain_scores_codex":[0.9994828,0.00005734741,0.0000242858,0.00009535693,0.0001932475,0.0001470003],"domain_scores_gemma":[0.9970924,0.0006815896,0.0003448957,0.0001095753,0.001534438,0.0002370657],"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":[0.0006504227,0.0002008299,0.4046783,0.0001496327,0.0002448003,0.0002048368,0.0004762185,0.4581879,0.001316278,0.00320485,0.02875418,0.1019318],"study_design_scores_gemma":[0.00001747327,0.00004297655,0.1465204,0.00003724169,0.0000378662,0.0000192068,0.0002321157,0.8486124,0.0005749653,0.0007271261,0.003132784,0.00004555012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733822,0.0004980045,0.009644031,0.001587717,0.0001219497,0.00004691605,0.01114044,0.000444076,0.003134551],"genre_scores_gemma":[0.9878073,0.0002458458,0.003118336,0.00008421986,0.0000321769,0.00002648219,0.005933525,0.00002732634,0.002724765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0498867,"threshold_uncertainty_score":0.1003609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1750862598526652,"score_gpt":0.306841124187358,"score_spread":0.1317548643346927,"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."}}