In-Stream Temperature Modeling to Evaluate Potential Management Practices along the Speed River, Southern Ontario
Bibliographic record
Abstract
The temperature of the Speed River near the City of Guelph in Southern Ontario is affected by an upstream reservoir, urban runoff, reduced baseflows, in-stream impoundments and development of riparian areas. Mitigating stream temperature increases in this system is desirable because of the direct effects of increased temperature on aquatic organisms but also because of the relationship between stream temperature and dissolved oxygen solubility. Dissolved oxygen levels downstream of the City's wastewater treatment plant are of particular concern despite high levels of treatment at the plant. Identification of other, less costly, means to sustain acceptable dissolved oxygen levels in the Speed River, in conjunction with nutrient control, would be of interest. The Stream Network Temperature Model (SNTEMP) was linked to a hydrologic watershed model (GAWSER) and applied to the study area to obtain preliminary predictions of the effects of various management practices on the thermal regime of the Speed River. A network of stream temperature loggers was used to calibrate the stream temperature model and identify specific locations on which to focus management efforts. Management practices evaluated were increased riparian vegetation, increased flows from an upstream reservoir, removal of existing in-stream impoundments and decreased stream width. Improvement of the performance of the model is desired along with application of a stream temperature model with a finer temporal resolution.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".