Multivariate Modelling of Water Temperature in the Okanagan Watershed
Bibliographic record
Abstract
Initiatives for the protection of river ecosystems must include the monitoring of key flow and water quality variables, as well as clear and quantifiable management goals. One variable which strongly influences water quality is water temperature, and its modification arising from human activity should be incorporated into ecosystem protection guidelines. This work, conducted as part of the Canadian National Agri-Environmental Standards Initiative (NAESI) program, presents a preliminary study investigating statistical regression methods and a geostatistical approach to model key water temperature characteristics that could assist in the development of standards. Water temperature time series recorded at 16 sites in the Okanagan watershed were used to develop the models. Monthly maxima were modelled for the period of April-September 2007 using four predictors: the site longitude, the drainage basin maximum altitude, the local slope at the station, and the log of the mean substrate diameter. Four types of multivariate regressions of monthly maxima were produced, and a leave-one-out resampling approach was used to validate the models. Relative Bias, Root Mean Square Errors (RMSE) and a corrected Akaike Information Criterion (AICc) were calculated for each month. Models gave RMSE values between 0.9°C and 2.1°C for the monthly maxima. All models generally performed best between May and July. Geostatistical interpolation of maxima was also performed in a multivariate physiographic space reduced to two orthogonal dimensions using canonical correlation analysis (CCA). Examples of interpolated maps show that the approach can be used to discriminate between warm and cool streams.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".