Internal Controls Over FY 2007 Army Adjusting Journal Vouchers
Bibliographic record
Abstract
Department of the Army and Defense Finance and Accounting Service (DFAS) personnel who are responsible for Army financial reporting should read this report. It discusses the adequacy of internal controls over the review and approval of adjusting journal vouchers made to Army accounting data during the third quarter of FY 2007. We performed this audit in support of Public Law 101-576, the "Chief Financial Officers Act of 1990," November 15, 1990, as amended by Public Law 103- 356, the "Federal Financial Management Act of 1994," October 13, 1994. This report is a result of work performed in support of the audits of the FY 2007 Army General Fund and Army Working Capital Fund Financial Statements. The third quarter FY 2007 Army General Fund Financial Statements reported total assets of $304.4 billion, total liabilities of $70.0 billion, and budgetary resources of $268.4 billion. The third quarter FY 2007 Army Working Capital Fund Financial Statements reported total assets of $23.0 billion, total liabilities of $ 1.4 billion, and budgetary resources of $19.3 billion. Maintaining internal controls over the preparation and recording of accounting adjustments has been a long-standing problem at Defense Finance and Accounting Service Indianapolis Operations (referred to as "DFAS Indianapolis").
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.157 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".