An approach for assessing cumulative effects in a model river, the Athabasca River basin
Bibliographic record
Abstract
Novel approaches addressing aquatic cumulative effects over broad temporal and spatial scales are required to track changes and assist with sustainable watershed management. Cumulative effects assessment (CEA) requires the assessment of changes due to multiple stressors both spatially and temporally. The province of Alberta, Canada, is currently experiencing significant economic growth as well as increasing awareness of water dependencies. There has been an increasing level of industrial, urban, and other land-use related development (pulp and paper mills, oil sands developments, agriculture, and urban development) within the Athabasca River basin. Much of the historical water quantity and quality data for this basin have not been integrated or analyzed from headwaters to mouth, which affects development of a holistic, watershed-scale CEA. The main objectives of this study were 1) to quantify spatial and temporal changes in water quantity and quality over the entire Athabasca River mainstem across historical (1966–1976) and current day (1996–2006) time periods and 2) to evaluate the significance of any changes relative to existing benchmarks (e.g., water quality guidelines). Data were collected from several federal, provincial, and nongovernment sources. A 14% to 30% decrease in discharge was observed during the low flow period in the second time period in the lower 3 river reaches with the greatest decrease occurring at the mouth of the river. Dissolved Na, sulfate, chloride, and total P concentrations in the second time period were greater than, and in some cases double, the 90th percentiles calculated from the first time period in the lower part of the river. Our results show that significant changes have occurred in both water quantity and quality between the historical and current day Athabasca River basin. It is known that, in addition to climatic changes, rivers which undergo increased agricultural, urban, and industrial development can experience significant changes in water quantity and quality due to increased water use, discharge of effluents, and surface run-off. Using the results from this study, we can begin to quantify dominant natural and man-made stressors affecting the Athabasca River basin as well as place the magnitude of any local changes into an appropriate context relative to trends in temporal and spatial variability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".