Exploring restoration efforts from a social lens: statistical models reveal relationships between salmon habitat restoration efforts and ecological and social characteristics of the Puget Sound basin, USA
Bibliographic record
Abstract
Habitat restoration is an important tool for promoting the conservation and recovery of imperiled species and is motivated by both environmental and social factors. As new restoration efforts are considered, it is important to look back and see what can be learned from past efforts, including how restoration benefits are distributed across communities through an equity lens. Focusing primarily on the Puget Sound basin in the state of Washington, this study investigates correlations between environmental and social factors and the spatial distributions of past restoration efforts. We specified statistical models to explain the variation in the number of restoration worksites undertaken in subwatersheds as a function of environmental and social variables. Using a common set of explanatory variables, we fit four models to examine the distribution of worksites associated with particular types of restoration actions (instream, riparian, land acquisition, and fish passage) and a fifth model to examine the distribution of all aquatic-based restoration worksites across action types. The results reveal statistically significant relationships between the number of worksites and several environmental characteristics, including elevation and species richness number of the Salmon Evolutionary Significant Units. Among the social explanatory variables, the percentage of non-Hispanic white residents in a subwatershed was the most prominent predictor of the number of restoration worksites across models, producing positive and statistically significant estimated coefficients in the instream, riparian, and total worksite models. We also estimated the specified models using data from other populated drainage basins in the region and found corroborating results for some patterns revealed in the Puget Sound basin. Our results provide insight for consideration when planning future restoration effects. With the knowledge of potential past social inequalities and inequities, restoration managers can, moving forward, take appropriate steps to account for these disparities.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".