Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Twelve cumulative effects assessments (CEAs) have been conducted at Hydro-Québec since 1999. This article explains how they have evolved in a seven-step approach. It also describes the problems encountered and solutions found for each of these steps. Hydro-Québec's CEAs focus on historical and regional perspectives, including a detailed past baseline description. However, there is no specific methodology proposed for significance determination, and possibly no need for it. CEAs provide a broader view that is found useful to assess impacts sometimes not properly tackled at the project level, but the question of how the promoter should conduct follow-up and mitigation efforts in the context of cumulative effects is still open. CEA must be a separate section of an impact assessment with its own methodology, spatial and temporal scales. Only certain environmental components should be examined. A well-documented past baseline condition is essential. Future effects can rarely be predicted over a ten-year period when combined with other impact sources. Cumulative effects with other future projects are difficult to determine when no direct impact can be found at the project level.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it