Bibliographic record
Abstract
The navigation mission of the US Army Corps of Engineers entails maintenance and improvement of about 40,000 km of navigable channels serving 400 ports, including 130 of the nation's 150 largest cities. The Corps dredges an average annual 230 million cu m of sedimentary material at an annual cost of about $400 million (US). The Dredging Research Program (DRP) was conceived as an applied R&D program to meet documented needs of the Corps' division and district offices for technological advances to optimize its dredging activities. The $35 million (US), 7-year DRP was initiated in 1988. Many distinct products have been developed by the DRP for which annual and one-time direct and indirect benefits are quantifiable. For the first time in the US, a detailed study has been conducted to accurately quantify and document the economic benefits of a federal R&D program. The DRP applied private industry uncertainty analyses to quantify in dollars Corps-wide use of DRP products. A computerized Monte Carlo simulation, skewed to the right, allowed conservative benefit certainty to be developed from DRP benefit estimates. The certainty simulations provide a 90 percent confidence level ascertaining that application of DRP products will benefit the US at least $14 million (US) annually in 1994, and at least $109 million (US) by 1998, in 1994 dollars.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.992 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".