Designing an Outfall Extension Through a Beach Renourishment for Deal Lake, New Jersey
Bibliographic record
Abstract
The New York District Corps of Engineers has embarked on a major, and ongoing effort to renourish the beaches of northern New Jersey as a cost-effective means of controlling shoreline damage. However numerous existing shoreline structures bisect the nourishment project, inhibiting the natural migration and restoration of the beach back to a continuous, straight shoreline. Various strategies have been applied to compensate for these structures including burial, notching, shortening, and even lengthening. One particular structure is uniquely challenging in that not only does it drain an upland lake, but it also is a migratory path for fish. The Deal Lake outfall is the only pathway for herring to return from the sea to spawn in the lake. To develop a solution for this structure that could bypass sediment to maintain a straight shoreline, yet also create and sustain an open migratory path for fish back up the outfall, a Value Engineering study was initiated at the concept level. The study addressed geometric effects of structure shape on sand bypassing, fish swimming and maneuvering capabilities, and public recreation and interest considerations. Various concepts ranged from systems of fish ladders, to fully submerged outfalls with lighting to orient fish, to jetties spurs, and scour inducing groin heads. A preliminary design concept was constituted from the Value Engineering recommendations and extensively tested in a movable bed physical model. Behavior of the outfall system to bypass sediment under wave loadings from various directions was monitored. The ability for the outfall to remain clear of sediment was enhanced using a concept of reflected wave action to promote sediment agitation. A final design was developed which employed a notch to an existing groin, a downstream spur situated just landward of the notch, and a self-scouring outfall pipe issuing from the spur.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".