Predicting spatial distributions of sediments in fluvial environments using kriging and flowline based distances.
Bibliographic record
Abstract
The process of predicting reasonable spatial distributions of surficial sediments in fluvial environments using interpolation techniques can be quite challenging. The process of sediment transport creates anisotropy in the spatial distributions that follow the direction of flow. Standard geostatistical software only allow for the incorporation of one anisotropy direction for the entire problem domain. This is problematic for fluvial environments because an average direction usually misrepresents the local changes in anisotropy throughout the river. A distance transformation technique based on flowlines was applied in an attempt to improve the way geostatistical algorithms deal with anisotropy. The standard geostatistical method of ordinary kriging was modified so that distances measured along, and perpendicular to, flowlines are substituted for the Cartesian coordinate system distances typically used. Five simple test cases were generated using the CH3D hydrodynamic model with sediment transport to examine how the method performs in idealized environments. (Abstract shortened by UMI.)Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .W56. Source: Masters Abstracts International, Volume: 43-01, page: 0269. Advisers: Stanley Reitsma; Phil Graniero. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".