Managing St. Lawrence River discharge in times of climatic uncertainty : How water quantity affects wildlife, recreation and the economy
Bibliographic record
Abstract
Large inland lakes with closed drainage basins are largely dependent upon the climatic regime for their water balance, both of which determine the seasonal and inter-annual variations in lake level. Climatic conditions and the resulting changes in water availability impact human activities; in turn, human adaptation to climate (especially under drought conditions) can exert significant feedbacks on water resources. This situation is exemplified by the major changes experienced by two of the largest inland lakes of the world : increased water supply prompted a 2.7 m rise in the Caspian Sea level from 1978 to 1998 (+ 0.135 m per year) whereas persistent drought and water diversions for irrigation purposes generated a 20 m drop in the Aral Sea level between 1960 and 2000 (– 0.5 m per year) (Jorgensen et al. 2003). As a consequence, the Aral Sea has lost over 75 percent of its original (pre-diversion) surface area of 68 000 km2 and split into two basins in 1989 (Jorgensen et al. 2003). These two contrasting situations illustrate the extreme vulnerability of inland lakes to climate and human interventions. Although the situation in the North American Great Lakes differs in many respects, they are nevertheless subject to the same kinds of interactions between climate, hydrology and human activities—all of which bear consequences for aquatic ecosystems (Mortsch 1998, Schindler 2001). As the St. Lawrence River constitutes the major outlet of the Great Lakes through Lake Ontario, the hydrological regime experienced in the river is largely tied to the climatic conditions and human activities taking place in the upper part of its watershed – at the continental scale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".