Context: Marine Environmental Quality and the Oceans Act
Bibliographic record
Abstract
The Oceans Act was proclaimed in January of 1997, and provides a legislative framework for the management of Canada’s oceans and marine resources. Part II calls for integrated management plans, the development of a national system of marine protected areas, and the establishment of marine environmental quality guidelines, objectives and criteria. In this context, three interconnected Oceans Act programs are presently being undertaken by the Department of Fisheries and Oceans (DFO): Inte-grated Management (IM), Marine Protected Areas (MPA), and Marine Environmental Quality (MEQ). What is Marine Environmental Quality? The definition of Marine Environmental Quality (also known as Marine Ecosystem Health) has been a subject of debate for many years. For the purposes of the MEQ Program, DFO has adopted the definition from Skjoldal (1999) stating that Marine Environmental Quality:... is an overall expression of the structure and function of the marine ecosystem taking into account the biological community and natural physiographic, geo-graphic and climatic factors as well as physical and chemical conditions includ-ing those resulting from human activities. Figure 1 conceptualizes the overall goals of the DFO Marine Environment Quality Program. Components of Marine Environmental Quality
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".