Turbidity associated with the beneficial placement of dredged sediment in the dynamic littoral zone at Fairport Harbor, Ohio
Bibliographic record
Abstract
The nearshore placement of sediments dredged via maintenance of navigation channels is a cost-effective management solution which offers myriad benefits that can include shoreline protection and habitat restoration. Benefits can be offset by concerns due to potential effects on the ecosystem from turbidity generated during placement. This study investigates the turbidity and geomorphic response of dredged sediment placed within the dynamic littoral zone of Lake Erie. Turbidity was measured at nine locations before, during, and after nearshore dredged material placement, and bathymetry was measured in four surveys to capture the effects of placing 39,400 m 3 of material from Fairport Harbor, Ohio. Nearshore turbidity measurements collected before dredging occurred could be explained using wave data, which provided an estimate for expected background turbidity during and after dredging. These wave models continued to fit measured turbidity data well during and after dredged material placement and indicate sediment placement did not significantly increase turbidity at any of the monitoring locations. Bathymetry surveys indicate sediment was placed within the active littoral zone and may have contributed to accretion along the onshore edge of the pre-existing bar. These observations indicate that nearshore placement of dredged material in this environment is aligned with natural sediment movement in the littoral zone, with minimal to negligible generation of turbidity beyond what occurs naturally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".