Recovery Act: Most DOE Cleanup Projects Are Complete, but Project Management Guidance Could Be Strengthened
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "From October 2009 through March 2012, the number of full-time equivalent (FTE) employees funded by the American Recovery and Reinvestment Act of 2009 (Recovery Act) and working on Department of Energy's (DOE) Office of Environmental Management (EM) cleanup projects peaked at about 11,000 FTEs in the quarter ending September 2010, according to data on the federal government's Recovery Act website. By the second quarter of fiscal year 2012, as projects were completed, FTEs had decreased to about 1,400 FTEs; 12 of 17 sites reported no Recovery Act FTEs; and about $5.6 billion of a total $6 billion in Recovery Act funds had been spent. According to EM data, as of April 30, 2012, 78 of the 112 Recovery Act-funded cleanup projects were complete, and 72 of the 78 projects met DOE's performance standard of completing project work scope without exceeding the cost target by more than 10 percent."
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.039 |
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".