Data for: Efficiency assessment of hydroelectric power plants in Canada: A multi criteria decision making approach
Bibliographic record
Abstract
Abstract of associated article: Hydropower plays a major role in the Canadian electricity generation industry. Few attempts have been made, however, to assess the efficiency of hydropower generation in Canada. This paper analyzes the overall efficiency of hydropower generation in Canada from comprehensive viewpoints of electricity generating capability, its profitability, as well as environmental benefits and social responsibility using the TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution) method. The factors that influence the efficiency of the hydropower generation are also presented to help to the sustainable hydropower production in Canada. The most important results of this study concern (1) the pivotal roles of energy saving and of the social responsibility in the overall efficiency of hydropower corporates and (2) the lower hydropower generation efficiency of some of the most important economic regions in Canada. Other results reveal that the overall efficiency of hydropower generation in Canada experienced an improvement in 2012, following a downtrend from 2005 to 2011. Amidst these influencing factors, energy saving and social responsibility are key factors in the overall efficiency scores while management (defined herein by the number of employees and hydropower stations of a corporation) has only a slightly negative impact on the overall efficiency score.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".