Habitat Restoration and Environmental Remediation Success at a National Wildlife Refuge Wetland
Bibliographic record
Abstract
The Lake River Industrial Site (LRIS), owned by the Port of Ridgefield (Port) in Ridgefield, Washington, USA, was home to a wood-treating facility from 1964 to 1993. The wetland habitat of Carty Lake immediately adjacent to the LRIS was found to be contaminated with dioxins and other wood-treating chemicals, likely a result of discharge from former stormwater outfalls. To inform the design of the cleanup action, Maul Foster and Alongi, Inc. (MFA) applied incremental sampling methodology (ISM) in wetland sediments. The ISM results were used to focus the active remediation to a limited area. MFA designed, permitted, and provided oversight for implementation of the remedial action, which involved temporarily dewatering and excavating sediment from 0.6 hectare of wetland. Through both careful design and an extensive grading effort, the restored wetland surface retained the nuanced topography and “pocket-habitats” of the preconstruction conditions. An approximately 550-meter-long failing bulkhead was also permanently stabilized in place by the construction of bioengineered soil embankments. The design and implementation also included significant landscape components; the wetland, transitional zones, and upland areas of the site were completely revegetated with native plant communities to provide wildlife habitat, prevent erosion, and restore culturally significant plants to an area historically used by Native American tribes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".