Estimating the benefits of widespread floodplain reconnection for Columbia River Chinook salmon
Bibliographic record
Abstract
In the Pacific Northwest, widespread stream channel simplification has led to a loss of habitat area and diversity for rearing salmon. Subsequent efforts throughout the Columbia River basin (CRB) have attempted to restore habitats altered through land development to recover imperiled salmon populations. However, there is scant evidence for demographic change in salmon populations following restoration. We used a process-based approach to estimate the potential benefit of floodplain reconnection throughout the CRB to Chinook salmon (Oncorhynchus tshawytscha) parr. Using satellite imagery, we measured stream habitats at 2093 CRB stream reaches to construct random forest models of habitat based on geomorphic and regional characteristics. Connected floodplain width was the most important factor for determining side channel presence. We estimated a current CRB-wide decrease in side channel habitat area of 26% from historical conditions. Reconnection of historical floodplains currently used for agriculture could increase side channel habitat by 25% and spring Chinook salmon parr total rearing capacity by 9% over current estimates. Individual watersheds vary greatly in habitat factors that limit salmon recovery, and large-scale estimates of restoration potential like these are needed to make decisions about long-term restoration goals among imperiled populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".