ASSESSMENT OF METHODS FOR MEASURING EMBEDDEDNESS: APPLICATION TO SEDIMENTATION IN FLOW REGULATED STREAMS<sup>1</sup>
Bibliographic record
Abstract
Abstract:Five commonly used methods for measuring embeddedness the — degree to which fine particles surround coarse substrate on the surface of the streambed — are assessed and used to evaluate the sedimentation pattern resulting from impoundment on tributaries of the Connecticut River. Results show that the U.S. Environmental Protection Agency (USEPA) method best reflects the sediment regime on these rivers. On the Ompompanoosuc River, regulated by a run‐of‐the‐river/flood control dam, embeddedness increases significantly directly downstream of the dam. On the unregulated White River, no downstream trends in embeddedness are observed. The USEPA results on the Ompompanoosuc River reflect the movement of a local decrease in embeddedness, interpreted as a moving region of scour, with a calculated transport rate of approximately 5 to 25 m/day. Observed transport rates are similar to previously measured sediment transport rates and consistent with results from a multifraction sediment transport model. Application of the USEPA method to an additional regulated tributary demonstrates the effects of dam management on embeddedness. Flow regulation with high sediment trapping efficiency results in a decrease in embeddedness downstream of the dam. Results provide insight into the utility of available methods for evaluating the effects of management practice on streambed composition.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".