Bibliographic record
Abstract
Spur dikes are river engineering structures that project from the bank of a stream at some angle to the main flow direction. They are principally used for river training and protection of the riverbank from erosion. A spur dike might be considered a form of macroscale boundary roughness, which produces a backwater effect upstream from the spur dike location. Despite this impact, spur dike design often proceeds without regard to the effect that the spur dike might have on the stream system. The work presented herein is on the backwater effect due to a single, vertical-walled spur dike. It is based on a momentum analysis in which the resistance offered by the spur dike is represented by a drag equation, for which the key parameter is the spur dike drag coefficient. Experimental data acquired for various configurations of a single spur dike within fixed-bed flumes have been used to calibrate and validate the proposed backwater model. The results show that the spur dike drag coefficient, hence the computed backwater effect, depends on the channel contraction caused by the spur dike, the degree of spur dike submergence, the aspect ratio of the spur dike, and the Froude number of the flow.Key words: spur dike, backwater effect, physical model, momentum principle, drag force, drag coefficient, river engineering.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".