The Gulf of St. Lawrence Marine Ecosystem: An Overview of its Structure and Dynamics, Human Pressures, and Governance Approaches
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The Estuary and Gulf of St. Lawrence (EGSL) represents one of the largest and most productive estuarine/marine ecosystems in Canada and in the world. However, the EGSL ecosystem is affected by a wide variety of human activities that pose significant threats to its integrity and the sustainable use of its resources. These include fisheries, navigation, mariculture activities, coastal development, recreational use (including marine mammal observation), climate change, and several land–based activities that occur along the EGSL shores and in coastal and upstream rivers and tributaries, including industrial and municipal activities, agriculture, and river damming (for water level control and hydropower). In 2005, the Government of Canada initiated the Oceans Action Plan (OAP) in order to implement an ecosystem-based management approach for five Large Ocean Management Areas (LOMAs), including the Estuary and Gulf of St. Lawrence. This presentation provides an overview of the EGSL ecosystem structure and functioning, as well as its human pressures, through a summary of the scientific tools that were developed within this initiative. As well, a brief description of governance approaches currently being developed in the area to implement these tools is also provided.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".