CREATING AND EVALUATING DIGITAL ELEVATION MODEL‐BASED STREAM‐POWER MAP AS A STREAM ASSESSMENT TOOL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT As urban development increases, a need is emerging to understand and predict river behaviour in order to focus rehabilitation efforts and protect the natural river system while preserving urban infrastructure. Stream assessment methods are reviewed to demonstrate the need for a physically based and objective method that is also accessible in terms of time, data requirements and expertise. The case of Highland Creek near Toronto, Canada, is used to demonstrate a new type of initial stream assessment method that is based on the concept of stream power and performed entirely in a geographic information system using information from a digital elevation model (DEM). The results from this analysis are tested against existing information for Highland Creek. This includes a hydraulic model (Hydraulic Engineering Center's ‘River Analysis System’), field‐measured slopes, air photos and the geomorphic effects of an extreme flood. In addition, the results are presented in map form to demonstrate the effectiveness of visualizing the stream‐power distribution over the entire basin and also the usefulness of overlaying stream power onto other available information. The slopes extracted from the DEM are found to be statistically similar to those from a one‐dimensional hydraulic model and field‐measured slopes. Individual peaks in slope as well as locations of stream‐power maxima and minima are found to correlate to actual channel features as seen in air photos. The extreme flood event of August 2005 caused a dramatic change in channel form at the exact location of maximum energy predicted by the DEM‐based stream‐power analysis. The case of Highland Creek illustrates how this approach yields a useful outcome for understanding stream dynamics and stability as part of a stream assessment process. Copyright © 2011 John Wiley & Sons, Ltd.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| 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 it