Multiple land use activities drive riverine salinization in a large, semi‐arid river basin in western Canada
Bibliographic record
Abstract
Abstract Salinization is increasingly recognized as a global issue. However, the relative importance of different drivers across a broad range of ions and ecosystems is not well understood. This study examined spatial and temporal dynamics in riverine salinity (conductivity, Ca2+, Mg2+, Na+, K+, , Cl− and ) in the South Saskatchewan River Basin (SSRB), a semi‐arid, mixed land use watershed in Alberta, Canada. A significant temporal increase (p < 0.05) in the concentration of one or more ions was observed at all 12 study sites. While all ions exhibited a significant increase in concentration over time, the rate of change was generally highest for Cl− (≈ 1.4–3.0% yr−1). The observed increase in riverine Cl− loading downstream of a large urban center (≈ 1700 tonnes yr−1) was attributed to increasing inputs from road salt (≈ 1800 tonnes yr−1) and to a lesser extent municipal wastewater (≈ 400 tonnes yr−1). For most other salts, spatial variation was driven not by urbanization but by the proportion of salt affected soils and/or cropland. A distinct Na2SO4 signal was observed at stations draining salt affected soils which strengthened over time at 7/12 sites indicating temporal trends in Na+ and have been driven largely by soil processes. A strong relationship between cropland and salt chemistry across the basin suggests agricultural activities have also contributed to observed trends. Therefore, in regions with similar climatic and anthropogenic characteristics to the SSRB, multiple stressors are likely to be operating and as such, these systems may be at particular risk from salinization.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".