Developing and applying control charts to detect changes in water chemistry parameters measured in the Athabasca River near the oil sands: A tool for surveillance monitoring
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Bibliographic record
Abstract
Control charting is a simple technique to identify change and is well suited for use in water quality programs. Control charts accounting for covariation associated with discharge and in some cases time were used to explore example and representative variables routinely measured in the Athabasca River near the oil sands area for indications of change. The explored variables include 5 major ions (chloride, sodium, sulfate, calcium, magnesium), 5 total metals (aluminum, iron, thallium, molybdenum, vanadium), and total suspended solids at two sites straddling the developments north of Fort McMurray. Regression equations developed from reference data (1988-2009) were used to predict observations and calculate residuals from later test data (2010-2016). Evidence of change was sought in the deviation of residual errors from the test period compared with the patterns expected and defined from probability distributions of the reference residuals using the odds ratio. In most cases, the patterns in test residuals were not statistically different from those expected from the reference period at either site, especially when data were examined annually. However, differences were found at both locations, more were found at the downstream site, and more differences emerged as data accumulated and were analyzed over time. In sum, the analyses at the downstream site suggest higher concentrations than predicted in most major ions, but the source of the changes is uncertain. In contrast, the concentrations of most metals at during the test period were lower than expected, which may be related to deposition patterns of materials or weathering of minerals during construction activities of the 2000s which influence the reference data used. The analyses also suggest alternative approaches may be necessary to understand change in some variables. Despite this, the results support the use of control charts to detect changes in water chemistry parameters and the value of the tool in surveillance phases of long-term and adaptive monitoring programs. Environ Toxicol Chem 2018;37:2296-2311. © 2018 SETAC.
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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 it