{"id":"W4399582083","doi":"10.32614/cran.package.tvmcomp","title":"tvmComp: Discounting and Compounding Calculations for Various Scenarios","year":2022,"lang":"en","type":"dataset","venue":"","topic":"Systems Engineering Methodologies and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Compounding; Discounting; Computer science; Econometrics; Psychology; Economics; Medicine; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001426861,0.00320187,0.001308459,0.005776365,0.0009096044,0.003593539,0.003806273,0.003328883,0.05849703],"category_scores_gemma":[0.01052305,0.0008123445,0.002857456,0.007093586,0.0003118368,0.0031125,0.00211083,0.002769862,0.08060034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002840229,"about_ca_system_score_gemma":0.001993983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02787809,"about_ca_topic_score_gemma":0.0583106,"domain_scores_codex":[0.9984747,0.0002812935,0.0002462214,0.0003762418,0.000425966,0.000195563],"domain_scores_gemma":[0.9973149,0.001067598,0.0002424313,0.000639887,0.0005848773,0.0001502774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008298118,0.00004368462,0.001227403,0.0008764412,0.00005267587,0.00004164656,0.00002112698,0.001802893,0.00007601459,0.001075751,0.9859967,0.008702692],"study_design_scores_gemma":[0.0003911835,0.00003936458,0.003824748,0.0006941269,0.00004966572,0.0002114507,0.000123721,0.009236555,0.0007305896,0.006296794,0.9783199,0.00008179337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005349166,0.0004454506,0.0004972304,0.0002077763,0.00007918601,0.00002911227,0.9937307,0.002360307,0.002115335],"genre_scores_gemma":[0.001500686,0.0003069745,0.001929362,0.00008663056,0.00001776754,0.0001205003,0.9946956,0.0002556391,0.001086829],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05849703,"threshold_uncertainty_score":0.1956921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03863002311772715,"score_gpt":0.2843855701763319,"score_spread":0.2457555470586047,"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."}}