Linking Marine Ecosystem Response to the Removal of Shoreline Armor and Large Dams in the Elwha River and Nearshore Environment of Washington, USA: An Extended Summary
Bibliographic record
Abstract
Shaffer, J.A.; Oxborrow, B.; Parks, D.; Bauman, J.; Michel, J., and Maucieri, D., 2024. Linking marine ecosystem response to the removal of shoreline armor and large dams in the Elwha River and Nearshore Environment of Washington, USA: An extended summary. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 890-895. Charlotte (North Carolina), ISSN 0749-0208. We assess the nearshore ecosystem processes and function and restoration response to removal of large scale in-river dams and shoreline-armor. Two nearly century-old large dams in the Elwha River watershed in the Northwestern United States were removed during 2011-2014, which liberated upwards of 18 million tonnes (Mt) or approximately ∼9 million cubic meters of silt, sand and gravel to sediment starved, armored and unarmored shorelines. Within one-year of the initiation of dam removal, unarmored shorelines in the drift cell broadened, flattened, sediment fined, and large woody debris (LWD) volumes increased, all significantly. Armored shorelines continued to be steep and coarse-grained. Beginning two years after dam removals approximately 4700 m3 of large riprap (shoreline armor) was removed from over 650 meters of the armored Elwha River east delta reach drift cell. Following armor removal, previously eroding shorelines broadened, sediment fined at the project site, LWD volumes increased significantly, and beach wrack metrics resembled non-armored beaches at the treatment reaches. Invertebrate communities also responded to both dam and armor removal, and showed increasing trends along the armor removal site every year of the study. We therefore conclude that armoring impairs ecosystem function along dam removal shorelines; and that removal of shoreline armoring along with dam removals can result in drift cell scale ecosystem restoration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".