Bibliographic record
Abstract
Rivers are considered as one of the main sources of surface water and important habitant of aquatic animals from effective inland ecosystems.River water, in addition to participation in Earth's climate cycle, is considered as one of the most important factors in the erosion of the planet Earth.In recent decades, significant progresses have been achieved in the engineering science.But in some cases, such as sediment transport, turbulent flows, flood control, and river response to environmental factors, engineers' society are still looking forward to precise method for calculating.The river network is defined as set of waterways that, in the basin level , discharges surface flows.Some of these waterways are as perennial rivers, and seasonal rivers, and some other as watercourse that only during rainfall, a flowing body of water follows.In order to provide mathematical model in determining Manning's roughness coefficient for mountainous range on the basis of existing field data, hydraulic and geotechnical information for 20 American river and 75moiuntain ranges related to Mr. Jarrett researches were collected.This is considered in choosing mentioned rivers that the sections have been placed on the mountainous range of the river, and the slope of the river at that location is more than 1%.The results obtained from the study, suggests that unlike Jarrett's proposed relations, soil mechanics factors are also have significant effects on the accuracy of the Manning's roughness coefficient, which is directly tangible in the results.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.961 | 0.968 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".