Modelling the river hydrokinetic energy at a bedrock depth constriction
Bibliographic record
Abstract
A study on the amount of river hydrokinetic energy (HKE) available at a bedrock depth constriction was conducted on the Spanish River in Ontario, Canada. Far fewer, if any, river HKE analyses have been conducted at locations where a depth constriction creates high energy flow conditions compared to the number of HKE studies where slope or width create high energy conditions. Thus, a HKE resource assessment was conducted to assess the feasibility of extracting energy resources for a nearby community and to better understand technical approaches for assessing energy resources caused by depth constrictions. The HKE of the study location was estimated according to the International Electrotechnical Commission (IEC) standards for river HKE resource assessment. A 1.5 km long section of the river was surveyed twice: on September 21, 2021 (110 m 3 /s) and April 28, 2022 (650 m 3 /s). An acoustic Doppler current profiler (ADCP) was used to collect data to facilitate the development, calibration, and validation of a hydrodynamic model. The flow velocity was found to exceed 1 m/s when the river flow was greater than 200 m 3 /s, corresponding to the 19% probability of exceedance for the river. Thus, turbine prototypes that have been targeting velocities lower than 1 m/s may be more suitable for the study reach and for other sites with similar velocity characteristics. The final feasibility indicator for river HKE resource is the annual energy production (AEP), which was calculated to be 1.9 MWh/year for this reach of the Spanish River.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".