Bibliographic record
Abstract
significance of an adverse effect and accordingly, we will make no such determinations;..." • This seems to be contrary to normal practice, as well as ignoring the requirements of the Principles of Sustainable Development (The Sustainable Development Act, Schedule A). • Please explain the meaning of this letter, and confirm that the Partnership agrees that the determination of significant adverse effects is a relevant factor in the CEC's review of the Project and is clearly within the Commission’s jurisdiction to do so. Response: The wording in the covering letter dated 2012 07 06 transmitting the Environmental Impact Statement (EIS) to Canada and Manitoba referred to in Question 1 is an attempt to point out the differences in the regulatory constructs of Canada and Manitoba and reflects one of the compromises reached in applying the “two-track approach. ” It is also meant to remind the reader of one of the difficulties in preparing an assessment for two different parties with differing requirements, responsibilities and emphasis. On the one hand, in the worldview of the Keeyask Cree Nations, all adverse effects on the environment are significant. On the other hand, environmental impact assessment in accordance with Federal technical guidance applying the wording of the Canadian Environmental Assessment Act requires a
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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.026 | 0.015 |
| Insufficient payload (model declined to judge) | 0.100 | 0.043 |
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".