Preliminary Assessment of Sediment Transport Dynamics Following Dam Removal: A Case Study
Bibliographic record
Abstract
Prediction of sediment transport dynamics following dam removal usually requires extensive field data, the collection of which requires both resources and time. Certain management decisions regarding dam removal, however, may be required before resources may be allocated to provide sufficient data to evaluate whether dam removal is a viable option. Here, we present a case study and demonstrate that some aspects of the sediment transport characteristics following dam removal can be evaluated with very limited information. The case study in question involves J.C. Boyle, Copco, and Iron Gate Dams on the Klamath River, California which together have an estimated 12 million m3 of sediment deposited in their reservoirs. With a reconnaissance field observation and upon examination of the very limited existing field data, we determined that it is possible to evaluate the potential for sediment deposition downstream of the dam following the removal of the dams under the worst-case-scenario assumptions. The evaluation was carried out with the help of DREAM-1, one of the Dam Removal Express Assessment Models developed at Stillwater Sciences. Results of the assessment indicate that potential sediment deposition would occur only for a brief period within approximately 10 km downstream of the dam with a maximum thickness of sediment deposition no more than 1.2 m.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".