Bibliographic record
Abstract
The growing evidence of climate change has posed challenges to environmentalists and general population around the world. The climate features which could have possibly changed are shorter winter, warmer annual average temperatures, and heavy rainstorms in summer. This study has been conducted to evaluate the effect of climate change on quantity and quality of stream flow by using Soil and Water Assessment Tool (SWAT) for the Silver Creek watershed, Ontario. The historical data (1970-2000) and future climatic data (2015 to 2044) were generated, using future A2 scenario. Techniques of SWAT calibration and validation for stream flow and water quality are developed and discussed, with particular attention paid to snowmelt, land cover factors, and soil type input values. The preliminary results show that future stream flow may have longer low flow periods extending from summer to fall, and severe annual water supply deficits may be possible for portions of the year in the 2030s. As a result, sediment transport capacity of reaches may decrease, and thus in-stream sediment deposition may be a concern causing increased bed levels in streams. Also, the future simulation results indicate that evapotranspiration is expected to increase resulting in reducing the amount of surface runoff. Also, an increase in future base flow shows that stream flow would be more dependent on groundwater contribution. In addition, the summer stream flows are not expected to increase in future due to climate change. The simulation of sediment and nutrient is in process and will be presented in the conference.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".