A geomorphic assessment to inform strategic stream restoration planning in the Middle Fork John Day Watershed, Oregon, USA
Bibliographic record
Abstract
A geomorphic assessment of the Middle Fork John Day Watershed, Oregon, USA, was used to generate a hierarchical, map-based understanding of watershed impairments and potential opportunities for improvements. Specifically, we (1) assessed river diversity (character and behavior) and patterns of reach types (and their controls); (2) evaluated the geomorphic condition of the streams; (3) interpreted their geomorphic recovery potential; and (4) synthesized the above into a hypothetical, strategic management plan. Collectively, these maps can set bounds and provide realistic guidance for river rehabilitation, design and implementation efforts. Fifteen distinct reach types were identified, two-thirds of which are found along perennial streams. On the basis of a variety of geo-indicators, approximately two-thirds of all perennial stream reaches were found to be in ‘good’ geomorphic condition, whereas one-third had departed to ‘moderate’ and ‘poor’ condition. Departures from ‘good’ condition were primarily related to riparian vegetation removal, conversion of floodplain to agricultural land uses (farming and grazing), logging, and channel bed dredge mining for gold. Encouragingly, the majority of reaches classified as being in moderate geomorphic condition were found to have high recovery potential. While our geomorphic assessment has practical utility for informing physically realistic expectation management for efforts like salmonid habitat restoration, the maps themselves are the key vehicle for communicating and visualizing among stakeholders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".