Water Availability: An Overview of Issues and Future Challenges for the St. Lawrence River
Bibliographic record
Abstract
For some years now, water availability in the St. Lawrence River has been an inherent part of the larger issues threatening the integrity of its aquatic ecosystems. The St. Lawrence system (including the Great Lakes) is among the three largest in North America, alongside the Mackenzie and Mississippi rivers, in terms of basin size and flow rate. Located downstream of the international section (Kingston to Cornwall, Ontario), the Quebec portion of the St. Lawrence comprises four major bio-geographic units: the fluvial section, which is primarily influenced by Great Lakes inflows, the freshwater tidal portion of the fluvial estuary, the saltwater transition located in the upper estuary and the lower estuary, which widens up to become the Gulf of St. Lawrence (SLC 1996). Despite its large size, the St. Lawrence is subject to a range of anthropogenic pressures including variations in water availability, which reveal its vulnerability to hydrological and climatic factors. To fully appreciate the issues at stake in the fluvial section, we must first examine the context in which the Great Lakes-St. Lawrence was transformed. Specific aspects relating to fluvial integrity will then be described using the case of Lake St. Pierre, a designated Biosphere Reserve (UNESCO) and Ramsar site, as an example. Lastly, we will examine the current pressures on the ecosystem and future risks to water availability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".