The effects of watershed characteristics and disturbance history on lake water quality of the boreal region of south easterrn Manitoba
Bibliographic record
Abstract
Water quality in remote lakes on the east side of Lake Winnipeg was largely unknown. This study was the first attempt at characterizing the baseline conditions in a large number of lakes on the east side of Lake Winnipeg. Water quality of up to 99 boreal shield lakes was sampled by floatplane in 2004 and 2005. Standard physical and chemical water quality data were collected for each lake. Water quality in this region was affected by characteristics of the watershed such as soil type, the proportion of wetlands in the watershed, and forest type. Watershed disturbance such as forest fire and forest harvesting appeared to have a marked influence on water quality. Forest harvesting disturbance and to a lesser extent fire disturbance was associated with the most eutrophic lakes. However, the most dramatic differences compared to reference sites appeared to only occur when a larger proportion of a watershed had been disturbed. The data indicated that if watershed forest harvesting had an impact on water quality it appeared to be of longer duration than that of forest fire.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".