Bibliographic record
Abstract
In February, 1990, the Secretary of Energy, James Watkins approved a grant for a waste (management) education and research consortium program by New Mexico State University (NMSU) to the US Department of Energy (DOE) . This program known by the acronym, WERC'' includes NMSU, the University of New Mexico (UNM), the New Mexico Institute of Mining and Technology (NMIMT), the Los Alamos National Laboratory and the Sandia National Laboratories. The program is designed to provide an integrated approach to the national need via the following: (1) Education in waste management by the Consortium universities resulting in graduate, undergraduate, and associate degrees with concentration in environmental management. The term waste management is used in a broad sense throughout this paper and includes all aspects of environmental management and environmental restoration. (2) Research programs at the leading edge, providing training to faculty and students and feeding into the education programs. (3) Education and research at the campuses, as well as from three field sites. (4) Ties with other multi-disciplinary university facilities. (5) Ties with two National Laboratories located in New Mexico. (6) Technology transfer and education via an existing fiber optic network, a proposed satellite link, and an existing state-wide extension program. (7) An outreach program to interest others in environmental management, especially precollege students, minority students and practitioners in the field. This report summarizes the accomplishments and status at the end of the first year.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.976 | 0.982 |
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".