Periphyton, water quality, and land use at multiple spatial scales in Alberta rivers
Bibliographic record
Abstract
The ability of land use to replace water quality variables in predictive models of periphyton chlorophyll a was tested with a 21-year data set for Alberta rivers. Nutrients (total dissolved P and NO2 + NO3) explained 23%24% of the variability in seasonal chlorophyll a, whereas land use (human population density) explained 25%28% of the variability. The best models included the combination of total dissolved P and population density, explaining 32%34% of periphyton chlorophyll a variability. However, analysis of variance of chlorophyll a by ecoregions and ecozones explained about as much variability (28%30%), and the inclusion of an ecoregion term into the regression models showed a diminished importance of land use as a predictor of chlorophyll a, with best models based on the combination of nutrients and ecoregion and explaining up to 43%44% of periphyton chlorophyll a variability. Within ecoregions, land use was sometimes a good surrogate for nutrient data in predicting chlorophyll a concentrations. Overall, land use is a suitable surrogate for nutrients in regression models for chlorophyll a, but its inclusion in general models may reflect regional differences in nutrientchlorophyll relationships rather than true land use effects on chlorophyll a.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".