Bibliographic record
Abstract
The CEC was created in 1994 by the North American Agreement on Environmental Cooperation (NAAEC), which was concluded by Canada, Mexico, and the United States as a side agreement to the North American Free Trade Agreement. The CEC’s mission is to facilitate collaboration and public participation to foster conservation, protection, and enhancement of the North American environment. The CEC is comprised of a council, the Secretariat, and the Joint Public Advisory Committee (JPAC). The council is composed of Cabinet-level environmental officials from each of the NAAEC parties and governs the CEC, approving the overall program and budget and implementing the NAAEC. The CEC Secretariat is headquartered in Montreal and has a liaison office in Mexico City. It is headed by an executive director, who oversees a co-operative work program on various North American environmental matters, the development of independent Secretariat reports on North American environmental issues, and the processing of public submissions on enforcement matters (SEM). A fifteen-member JPAC acts as an advisory body to the council on any matter within the scope of the NAAEC. In December, the council appointed the new executive director, César Rafael Chávez, for a period of three years, starting in January 2016 (more information can be found at < http://www.cec.org>).
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 0.018 |
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".