Benthic diatom communities in streams from zinc mining areas in continental (Canada) and Mediterranean climates (Portugal)
Bibliographic record
Abstract
This study compares regional differences in benthic diatom communities exposed to similar stresses in Canada and Portugal. Diatoms were sampled in the Água Forte Stream, Aljustrel (SW Portugal) and in the Little River, New Brunswick (SE Canada), both streams surround the respective zinc mine and are subject to similar metal (e.g. Cd, Cu, Fe, Zn) and acidic (Água Forte pH = 1.9–2.9 vs. Little River pH = 2.2–5.5) stresses. In this kind of extreme environment, diatoms are frequently the main algae group in the streams, widely used as bioindicators. Diatom communities in the Água Forte Stream were dominated mostly by Pinnularia aljustrelica and Eunotia exigua (5% teratological forms), whereas communities in the Little River were more diverse (e.g. Achnanthidium minutissimum, Nitzschia palea, Eunotia sp.). Shannon-Wiener Index (H′) and percentage of taxa relative abundance were used to characterize the diversity and species composition of the diatom communities. Using canonical correspondence analysis (CCA), it was found that regional variation in acceptable in-stream concentrations of metals, conductivity and pH were the primary drivers of benthic diatom community. Mine remediation to decrease metal concentrations and increase pH in streams will increase diatom diversity even in highly impacted streams such as Água Forte.
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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 | 0.000 |
| 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".