Bibliographic record
Abstract
Wind power is one of the fastest growing sources of electricity generation in the world. It is efficient, emissions-free and infinitely renewable. Developing renewable energy has been a key part of Suncor Energy’s long-standing climate change action plan – and also a part of the company’s ‘parallel path’ approach to energy production: responsibly developing the oil sands and other conventional sources of oil and gas while investing in new sources of energy for the future. A Canadian pioneer in wind energy, Suncor has six wind power developments in operation and other projects in the planning stages. During this presentation, we will provide a behind the scenes look at what it means to be Canada’s premier integrated energy company with a commitment to renewable energy. We will discuss our focus on responsibly developing wind energy resources, and review the overall process for Wind Power project development. We will discuss our focus on responsibly developing wind energy resources, and review the overall process for Wind Power project development. We will also discuss future renewable energy opportunities, and the critical role that collaboration with various stakeholders (including federal, provincial and municipal government, regulators, IESO’s, NGOs, and community/landowners) play in building a diversified energy future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".