{"id":"W4312277682","doi":"10.2139/ssrn.4236754","title":"Description and Download Link for Internal Rate of Return Data","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Download; Computer science; Econometrics; Economics; World Wide Web","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003725422,0.001581792,0.001909161,0.0105248,0.001192993,0.004191622,0.002433842,0.002304916,0.8309274],"category_scores_gemma":[0.02589443,0.001177992,0.001452317,0.007329576,0.0004345754,0.003619227,0.002791703,0.002294569,0.8046557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001638483,"about_ca_system_score_gemma":0.002139896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005721771,"about_ca_topic_score_gemma":0.003096704,"domain_scores_codex":[0.9975001,0.0002654974,0.000304486,0.0003160937,0.001164969,0.0004487206],"domain_scores_gemma":[0.9760672,0.006700496,0.001401566,0.004214942,0.008953697,0.002662054],"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":[0.0001746386,0.00009469841,0.0009971275,0.0002351712,0.000009720487,0.00002170435,0.0000287972,0.0002217023,0.0003164964,0.0007243341,0.9739628,0.02321282],"study_design_scores_gemma":[0.0004296863,0.0001875621,0.009098451,0.0004025867,0.00003975609,0.0001585419,0.0001564846,0.001151705,0.002271895,0.00313285,0.9828072,0.0001632814],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001149724,0.0001510161,0.003814043,0.0007653532,0.000864965,0.0006822826,0.8912832,0.01761775,0.08367161],"genre_scores_gemma":[0.008480163,0.0005231285,0.005667048,0.0008898014,0.0006054956,0.002312891,0.7158825,0.01442428,0.2512147],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8309274,"threshold_uncertainty_score":0.2411615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1253750151741829,"score_gpt":0.3631399739876527,"score_spread":0.2377649588134698,"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."}}