Renewable Energy Atlas and microgrid field testing in the Arctic
Bibliographic record
Abstract
Reducing the use of diesel to generate energy in remote northern communities is a major concern for the Canadian Government. It can reduce costs, help the environment, and improve life in the north. Much focus is often placed on integrating renewable energy technologies like wind, solar and biomass to replace diesel generation. However, there is still a need to better understand how clean energy can fit the way remote communities use energy. A better understanding of energy use also provides other benefits. It can help identify ways to save energy through conservation. It can also help identify other changes to energy use, like load management and peak load shifting. POLAR is aiding CanmetENERGY’s efforts to understand how renewable energies can be a larger part of the local electricity generation mix in remote communities. POLAR’s Alterative & Renewable team supports these efforts in Cambridge Bay, Nunavut. POLAR provides in-community support to field testing renewable energy microgrid and load management strategies. It is currently integrating and monitoring smart meters. POLAR will then help to compare the costs and benefits of different technologies for these strategies in Cambridge Bay.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".