Coastal Stream and Embayment Restoration Priorities along the BNSF Railroad: Results and Future Action
Bibliographic record
Abstract
The railroad right-of-way is a prominent modification along the eastern shore of the Washington portion of the Salish Sea. It runs along 52 miles of the shoreline, while another 73 miles of railroad is within 200 feet of the shoreline. In many places, the railroad forms a barrier between the coastal watershed and the shoreline, preventing the delivery of water, sediment, wood, and organic matter to the nearshore. This results in ongoing degradation of the habitat quality of the nearshore environment, which is important habitat for juvenile Chinook salmon and other salmonids. Because nearshore restoration along the railroad is expensive and requires extensive planning, restoration efforts should be focused on areas that would provide substantial benefits to fish habitat and nearshore processes. In order to identify these locations, this project completed a field inventory characterizing stream crossings and embayments along the railroad between the Nisqually delta and Canadian border. Field data was combined with available data to prioritize railroad stream crossings for existing function and restoration potential. Almost 200 stream crossings and 13 embayments were included in the final database. Thirteen data attributes were used to assess the habitat quality and potential of each site. An advisory team guided the development of the prioritization framework to ensure attributes were appropriately weighted and that it could support local and regional restoration outcomes of interest. Attributes were broken down into two categories: juvenile salmon use and upstream habitat quality. High scoring crossings in both categories represent ideal candidates for restoration attention. This assessment and prioritization of railroad crossings along the Puget Sound shoreline provides an important framework to guide strategic restoration in the Salish Sea.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".