Bibliographic record
Abstract
The Government of Canada's interdepartmental Program of Energy Research and Development (PERD) is managed by Natural Resources Canada and covers nearly all areas of non-nuclear energy. Its $57.6M/y budget is distributed across twelve federal departments and agencies, thereby enabling the federal government to effectively coordinate its energy research activities across all areas. PERD recently implemented a results-based management (RBM) system that annually subjects one-quarter of the program to an external evaluation and potential reallocation. PERD currently invests $4.75M annually in offshore R&D carried out by five federal departments and one agency. Activities are related to basin assessment and geotechnics in the Canadian North and offshore East Coast, winds-wave-current modeling, managing sea ice, iceberg and ice-structure interactions, ship design and navigation issues - including offshore safety, management of offshore drilling and production wastes, oil spills remediation and, finally, assessment of cumulative effects of wastes and produced waters. This paper details these activities, as well as future shifts in PERD to meet the S&T needs of the regulatory agencies and to protect the interests of the Canadian public.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".