Management of oil and gas activities in Canada's North and associated research
Bibliographic record
Abstract
One quarter of Canada's discovered (conventional) oil and gas resources are in the North and remain undeveloped. Managing the development of oil and gas resources for Canada's federal lands in the Northwest Territories, Nunavut and northern offshore is a federal responsibility. The Northern Oil and Gas Branch (NOGB) of Indian and Northern Affairs Canada (INAC) works in partnership with Northern and Aboriginal governments and communities to: govern the allocation of Crown lands to the private sector for oil and gas exploration; develop the regulatory environment; set and collect royalties; and approve benefit plans before development takes place in a given area. Petroleum resource management on Canada's federal lands north of 60 °N is exercised under two federal statutes: the Canada Petroleum Resources Act (CPRA) and the Canada Oil and Gas Operations Act (COGOA). The NOGB is responsible for the issuance and management of Exploration Licences, Significant Discovery Licences and Production Licences in the Northwest Territories, Nunavut and the northern offshore, pursuant to the CPRA. The NOGB plans and coordinates federally-funded science research that supports oil and gas management decisions in the North. This research works towards improving environmental, economic and social sustainability of oil and gas development in Canada's North, in a manner that is consistent with the Government's Northern Strategy as well as its responsibilities under the CPRA and the COGOA. The NOGB carries out research-related activities such as: leading research planning for the Beaufort Regional Environmental Assessment; representing INAC at the Environmental Studies Research Funds, and on the Frontier Oil and Gas Portfolio and associated programs under the Program of Energy Research and Development.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".