Benthic algae as bioindicators of agricultural pollution in the streams and rivers of southern Québec (Canada)
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of agricultural pollution on periphyton in streams and rivers of southern Québec. We sampled benthic algae incubated from mid-July to mid-August on artificial substrates at 29 sites and analysed the variations in community structure and total community biomass. Diatom community structure as well as total benthic algae community were analysed. Water samples were taken to provide background chemical information, and land use data were also obtained. Preliminary tests showed that colonisation of the artificial substrates (unglazed ceramic tiles) resulted in biomass levels (Chlorophyll a and ash-free dry weight) and species composition that were not statistically different from those on natural rock substrates. The canonical correspondence analyses showed that pH, conductivity and suspended solids were the most significant environmental variables accounting for variations among sites and diatom community structure. No additional resolving power was obtained by including cyanobacteria, green algae and flagellates. This total community analysis substantially increased variance and sample processing time while reducing the relationship with environmental variables. These results indicate that an analysis based exclusively on diatoms provided the optimal approach. Traditional nutrient measurements (phosphorus and nitrogen) did not explain a significant part of the variance in the species composition among sites. The ordination analyses clearly separated agriculturally-impacted streams from reference sites, but no significant grouping was observed related to the intensity and type of agriculture, indicating the greater importance of local farming practices. The use of periphyton as a bioindicator provides an integrated measurement of water quality as experienced by the aquatic biota, and therefore offers a useful addition to physico-chemical water quality monitoring strategies.
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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 | 0.001 | 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".