Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tetra Tech assists the U.S. EPA Great Lakes National Program Office (GLNPO) with design and construction management of sediment remediation projects in the Great Lakes region. Tetra Tech is currently managing two sediment remediation projects for GLNPO: the West Branch of the Grand Calumet River (WBGCR) in Hammond, Indiana, and the Ottawa River in Toledo, Ohio. The WBGCR flows through an industrialized area in northwest Indiana. Mechanical excavation of 32,000 cubic yards of sediment was completed in August 2010. The design for additional sections of the river is underway with construction planned for December 2010. The Ottawa River is part of the Maumee River Area of Concern in northwest Ohio. Hydraulic dredging of about 250,000 yards of sediment from the Ottawa River commenced in May 2010 and is scheduled to be completed in November 2010. The principal contaminant of concern is polychlorinated biphenyls at concentrations both above and below 50 ppm. This paper will compare and contrast the methods for sediment dredging, focusing on the specific site conditions that influenced selection of each approach as well as lessons learned during construction activities at both locations. Factors influencing remedy selection include sediment physical and chemical characteristics, site hydraulics, and site access.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.001 |
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 it