An empirical approach to predicting water quality in small streams of southern British Columbia using biogeoclimatic ecosystem classifications
Bibliographic record
Abstract
Water quality data from a synoptic survey of low-order streams (n = 581) were investigated as a function of the biogeoclimatic zone and moisture subzone groupings of the biogeoclimatic ecological classification (BEC) system. The potential utility of the BEC system as a watershed characterization tool was evaluated. The preliminary results were limited to streams sampled during June 1998 and 1999 over the large spatial scale of southern British Columbia. Significant differences (ρ < 0.05) were observed among biogeoclimatic zones and moisture subzones for specific conductance, turbidity, ph, and dissolved organic carbon (DOC) concentration. Our approach explained between 8 and 37% of the variation in water quality data, which could significantly reduce error in assessing water quality or investigating the effects of watershed activities among watersheds. The data provide a snapshot of water quality and identify areas that are likely to exceed water quality guidelines (ρ > 0.50). High proportions of low-order streams within the southern interior of British Columbia are likely to exceed water quality guidelines for turbidity and DOC content during a comparable sample period. Similarly, streams located in coastal areas of southern British Columbia exhibited ph values that were below the approved guideline of 6.5. Overall, the BEC system accounted for a significant amount of variation in water quality, suggesting that further development of this approach is warranted. The addition of other variables such as a history of land-use activities should be included, and data should be extended temporally to account for different flow regimes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".