Electricity Generation and Consumption In Canada
Bibliographic record
Abstract
The domain of interest is Energy; however, the focus is to observe the trends between the different sources used for electricity generation among Canada and its provinces from 2005 to 2016, and to compare the trends for electricity generation to electricity consumption in Canada from 2005 to 2015. The main problem that will be investigated is how much of a particular source is used for electricity generation in Canada over these eleven years and what is the least and most used source of electricity generation over Canada. It will also be observed whether the proportion of electricity generated by each source in Canada during 2016, is consistent with the proportion of electricity generated by each source in every province. Additionally electricity consumption for the provinces will be studied to determine which province consumes the most and least amounts of electricity in Canada. The significance of this problem is to understand which sources are highly used to generate electric power in the provinces and in Canada. If a source is being used the most in Canada and in the provinces, it will lead us to find possible ways to generate electricity from the least used sources, so the country and its provinces do not depend on one source for electric power. It will also be observed if the electricity generation by each province has increased, decreased or remain constant from 2005-2016. From this data we can also infer which province generates the most and least amount of electric power and determine which abundant resources are available to each province for its electricity generation. Moreover, by comparing the trends for electricity consumption and electricity generation it will be observed if any province consumes more electricity than it generates. If so we can find ways to provide that province with more electrcity by importing it from other provinces.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".