On the Large Scale Assessment of Small Hydroelectric Potential: Application to the Province of New Brunswick (Canada)
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
The mapping of the small hydropower (SHP) resource over a given territory is indispensable to identify suitable sites for the development of SHP renewable energy projects. In this study, a straightforward method to map the SHP potential over a large territory is presented. The methodology uses a synthetic hydro network (SHN) created from digital elevation models (DEM) to ensure precise hydro head estimations. From the SHN, hydro heads are calculated by subtracting the minimum from the maximum elevation of synthetic stream segments. Subsequently, stream segments with low hydro heads over a specified maximum distance are removed. Finally, the method uses regional regression models to estimate the annual baseflow for all drainage areas in the study area. The technical SHP potential can then be estimated as a function of the hydro head and maximum penstock length. An application of the method is made to the province of New Brunswick, Canada, where SHP maps have been developed to promote the development of the SHP energy sector in the province. In terms of the SHP opportunity, it is shown that the province of New Brunswick (71,450 km 2 ) has a good SHP resource. Using a representative hydro head (10 m) and penstock length (3,000 m) for the region, 696 potential sites have been identified over the territory. Results show that the technical SHP potential for New Brunswick is 368 MW for the conventional hydroelectric reservoir SHP configuration.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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