Hunting for High Head Hydro with HydroHelp 3
Bibliographic record
Abstract
Several high head run-of-river small hydro plants are currently being developed in British Columbia. Many of the sites were identified with the help of RETScreen®, an award-winning free program obtainable from www.retscreen.net. For more detailed cost assessments of hydro sites, the authors have developed a series of programs called HydroHelp, and the first, HydroHelp 1, for turbine selection is also available free at http://www.hydrohelp.ca. HydroHelp 3 is specifically developed for high head impulse site assessment, and this paper shows how desktop pre-feasibility assessments and cost estimates can be developed using Google Earth and the program, with only a few hours of work. HydroHelp 3 is a Microsoft Excel-based computer program developed with financial help from CANMET (Natural Resources Canada), to evaluate impulse-powered hydro sites. The program occupies just over 8.3MB, and has an input sheet where data from 186 input cells are used to develop a detailed 72-line cost estimate. The program output includes dimensioned generic drawings for all structures, calculates all project hydraulics and selects the most appropriate impulse turbine for the site from 13 types, including Turgo and cross-flow turbines. The program can be used on all impulse sites of more than about 1MW capacity. This paper includes an example on how to use the program to estimate the cost of the 10MW, 455m head site at Chipmunk Creek in south-west British Columbia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.105 | 0.017 |
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".