A framework for integrating climate change information with low flow estimation methods for Ontario streams
Bibliographic record
Abstract
In many parts of the world, freshwater resources are coming under stress due to increasing population, economic development activities and construction of dams and reservoirs to meet various societal needs. Alteration of natural river flow regimes due to the influence of anthropogenic activities has serious implications for aquatic, riparian, and wetland ecosystems. These pressures and activities are increasing overtime and therefore it is important to ensure river sustainability, integrity of associated ecosystems, and the well-being of humans who depend on the river for their livelihoods. These targets can be achieved by maintaining sufficient flows in the river during low flow periods so that the river can continue to provide all of its services. Freshwater resources are not only under stress due to the above mentioned pressures, they have also become susceptible to climate change. This is an emerging threat, which has drawn considerable attention from around the world and is also the main topic of this report. Among several impacts of climate change on low and high flow characteristics and seasonal water availabilities, it may also impact stream water temperatures and chemistry, as well as oxygen and nutrient contents of streams during low flow periods. Thus, the physical habitat of streams is also at risk due to future climate change.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".