Assessing large spatial scale landscape change effects on water quality and quantity response in the lower Athabasca River basin
Bibliographic record
Abstract
Increased land use intensity has been shown to adversely affect aquatic ecosystems. Multiple landscape stressors interact over space and time, producing cumulative effects. Cumulative Effects Assessment (CEA) is the process of evaluating the impact a development project may have on the ecological surroundings, but several challenges exist that make current approaches to cumulative effects assessment ineffective. The main objective of this study was to compare results of different methods used to link landscape stressors with stream responses in a highly developed watershed, where past work has shown that the river has experienced significant water quality and quantity changes to improve approaches to CEA. The study site was the lower reaches of the Athabasca River, Canada that have been subjected to a diverse range of intense anthropogenic developments since the late 1960s. Linkages between landscape change and river response were evaluated using correlation analyses, stepwise, multiple regression, and regression trees. Notable landscape changes include increased industrial development and forest cut-blocks, made evident from satellite imagery and supporting ancillary data sets. Simple regression analyses showed water use was closely associated with total phosphorus (TP) and Na(+) concentrations, as well as specific conductance. The regression trees for total organic carbon (TOC), TP, and Na(+) showed that the landscape variables that appear as the first characteristic were the same variables that showed significant relations for their respective simple regression models. Simple, stepwise, and multiple regressions in conjunction with regression trees were useful in this study for capturing the strongest associations between landscape stressors and river response variables. The results highlight the need for improved scaling methods and monitoring strategies crucial to managing cumulative effects to river systems.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".