Bibliographic record
Abstract
Mexico, the United States and Canada are three of the world’s most productive, diverse and interdependent economies in the world. In terms of energy systems, however, the three countries are incompletely interconnected and integrated in terms of infrastructure, reserves, operations and standards. This imposes costs, performance and security risks that affect GDP, labour and financial markets that could be mitigated through closer co-operative energy systems planning and interdependence. Future energy challenges facing the three countries, individually and collectively, are formidable. These include rising costs and increasing relative scarcity of both domestic and imported energy supplies, as well as environmental externalities associated with energy production and consumption. The three nations collectively possess a wide range of hydrocarbon reserves, underused non-renewable and renewable energy resources and untapped production and utilization technologies necessary for meeting future energy needs and mitigating the increasing impacts of climate change. Some co-operation is emerging between nations, such as the commitments from all three countries under the UN Climate Accord. Signed and announced in 2021, the commitments include 2030 targets on the path to net-zero emissions by 2050. While the adoption of global and sectoral carbon reduction goals by all three jurisdictions is positive, these commitments, supported by a tri-national carbon trading exchange, would incentivize the greater integration of North America’s energy markets through increased use of the lower carbon energy resources and technologies available in each jurisdiction. The result can be a more resilient energy sector, capable of meeting future demands for transportation, industry, heating and lighting loads that displace historically conventional energy supplies with renewable generation and alternatives that can support a future decarbonized region and world economy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.123 | 0.042 |
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".