{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002844625,0.0002281133,0.0003157164,0.0001338943,0.0002639998,0.00008822776,0.0002018634,0.0001219436,0.0005954332],"category_scores_gemma":[0.00006777529,0.0002328639,0.0000702919,0.00013605,0.00001876623,0.00004236036,0.0001024488,0.0002586132,0.000003410806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000985576,"about_ca_system_score_gemma":0.00001237825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001988978,"about_ca_topic_score_gemma":0.00005706156,"domain_scores_codex":[0.9991005,0.00002003142,0.0002877285,0.0002357279,0.0001080349,0.0002479848],"domain_scores_gemma":[0.9990008,0.0005434112,0.0000469285,0.0003511447,0.00001657601,0.00004110879],"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":[5.390557e-7,0.000005539791,0.000002409027,0.0003660178,0.00005352355,6.900385e-7,0.00001415583,0.08072133,0.00005952901,0.0004838039,0.9180934,0.0001989932],"study_design_scores_gemma":[0.0001126882,0.000008664714,0.00002130894,0.00002339022,0.00005632113,0.00001726757,0.00006143987,0.04013874,0.000003629795,0.00008083937,0.9592068,0.0002689406],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007735631,0.0003926065,0.1255427,0.00003442406,0.0007832721,0.0005839347,0.8721214,0.0003649454,0.0000994068],"genre_scores_gemma":[0.0002724523,0.0001201918,0.01678371,0.00002210994,0.0002369872,0.0007634475,0.9816186,0.00005499271,0.0001275171],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1094972,"threshold_uncertainty_score":0.9495915,"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."}}