Influence of climate and land use on nutrient and bacterial dynamics in surface waters of the Lower Fraser Valley, British Columbia
Bibliographic record
Abstract
It is understood that intensive agricultural activities can adversely impact surface-water quality resulting in risks to ecosystem and human health. What is less clear are the links between agricultural land use (type and intensity), environmental conditions and surface-water quality at varying spatial and temporal scales. There are also challenges with detecting agricultural influence on surface waters in a timely and accurate manner. This is of concern in the Lower Fraser Valley as this region has experienced significant agricultural intensification and population growth in recent years. This study examined influences of agricultural land use, climate and hydrology on water quality in three watersheds to identify land-use practices and environmental conditions producing the greatest risk of contamination. This was accomplished through an intensive surface-water sampling program to assess nutrient and bacterial dynamics in the Hatzic, Elk Creek and Salmon watersheds, combined with hydrometric and meteorological monitoring from 2002-2005. Spectroscopic techniques (absorption and fluorescence) were also evaluated as tools to detect and quantify agricultural influence. Consistent correlations between agricultural land use and contamination (nutrient and bacterial concentrations) were observed across all watersheds. Seasonal trends were consistent, with nutrient concentrations peaking during winter months (illustrating strong hydrological control over mobilisation and transport) and bacterial concentrations peaking during summer months (illustrating the supply-constrained nature of bacterial stores). Contaminant concentrations correlated with measures of agricultural intensity. Livestock operations represented the highest-risk land use for contamination, with even small operations producing observable impacts on water quality. Temporally, the greatest risk of bacterial contamination was associated with storm events preceded by periods of dry weather during summer months. Absorption and fluorescence were effective measures of agricultural influence as they quantify and characterize agriculturally-derived dissolved organic matter. Advantages of these techniques include rapid sample processing, minimal requirements for sample treatment and volume. Further, they provide qualitative information regarding water quality, water source and land use that is not available from nutrient or bacterial analyses alone. These techniques do not accurately detect contaminants in areas with minimal agricultural influence and therefore are limited as direct indicators of bacterial or nutrient concentrations.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".