Small land cover changes in the transboundary Kootenai River basin greatly alter water quality
Bibliographic record
Abstract
Abstract Increased nutrient loading in aquatic environments can have a long‐lasting influence on ecosystem processes and functions. The Kootenai River was historically oligotrophic, but nitrate levels have been steadily increasing since the mid‐2000s, while phosphorus levels have remained low. Our study objective was to evaluate the current nutrient distribution throughout the Kootenai River watershed in the context of land use and land cover change. Each of the three land cover types we assessed, agriculture, developed areas, and surface mines, encompass less than 1% of the land area in the Kootenai River watershed. We measured nitrate, ammonium, and soluble reactive phosphorus (SRP) monthly at sites spanning 400 river km on the Kootenai River, and co‐located tributary sites in British Columbia, Canada, and Montana and Idaho, USA. During July 2017, we measured the same nutrients along each of the selected tributaries with co‐located sites at sub‐catchment tributaries. Sites were selected to include a range of contributing drainage areas identified as agricultural, developed, or mining. Nutrient concentrations ranged from 0.012 to 4.299 mg/L for nitrate, 0.0005–0.02 mg/L for ammonium, and 0.003–0.076 mg/L for SRP. Nitrate concentrations were elevated downstream of mining land cover, and decreased with distance from the mining areas; however, nitrate remained elevated compared to sites not affected by mining activity. If the imbalance of N:P continues to increase due to nitrate loading, resources available to biota may become skewed, resulting in alterations to ecosystem processes and functions critical to support the biodiversity in the Kootenai River.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".