Analysis of Trends in Water Quality of Buffalo Pound Lake
Bibliographic record
Abstract
In this study various methods were used to understand the temporal behaviour of specific water quality variables of Buffalo Pound Lake in Saskatchewan. Various techniques were employed in trend determination and exponential smoothing techniques were employed in the modelling of fourteen water quality variables. Most of the variables were serially correlated, making the determination of the magnitude of trends unattainable at the 10% level of significance. The only variables with a statistically significant magnitude of trend were available nitrogen (−0.0066 mg/l.yr) and pH (−0.0072/yr). Upward trends in available phosphorus and silica, downward trends in chlorophyll α and sulfate were significant (p < 0.10), according to the Spearman Rank test. Temperature, dissolved oxygen, total dissolved solids, total suspended solids (upward) and colour were not significant (p > 0.10) using the Spearman Rank correlation coefficient. The Spearman Rank correlation coefficient for total phosphorus, and total suspended solids and colour (p < 0.18), suggest that these variables may be reporting the correct direction of the trend at least 82% of the time. Determination of time series models for each variable using exponential smoothing techniques yielded a variety of responses. Adequate models were obtained for temperature, dissolved oxygen, available nitrogen and total suspended solids based on the evaluation of forecast residuals. Models for total dissolved solids, pH and sulfate appeared on the borderline in their ability to predict effectively. No exponential smoothing models could be developed for available phosphorus and fecal coliforms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".