Characterizing deposited sediment for stream habitat assessment
Bibliographic record
Abstract
Numerous techniques are used to measure deposited sediment and quantify substrate quality in streams. We evaluated the relationship between land disturbance and stream habitat by comparing 25 commonly used deposited sediment parameters to watershed, riparian, and local‐scale drivers. We also tested whether land use regressions were improved by accounting for geomorphic setting (measures of slope and channel incision) and how visual versus measurement‐based estimations of percent fines and embeddedness were related to each other and to percent agriculture. Of the 16 metrics significantly related to watershed agriculture, subsurface percent fines was the best indicator of land use. Subsurface fines were more strongly related to both watershed and riparian percent agriculture than surface sediment metrics. The second best‐performing parameter was the visual assessment of percent fines <2 mm. Surface particle counts also performed moderately well. Sediment percentiles ( d 50 , d 84 ) and stability indices were among the weakest indicators of watershed land use. All measures of local percent agriculture were poor predictors of deposited sediment parameters. Mean slope within the entire stream network was nearly as good of a predictor of deposited sediment as watershed percent agriculture. This suggests that we may improve our ability to predict deposited sediment by considering land use within the appropriate geomorphic context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".