Are diatoms good integrators of temporal variability in stream water quality?
Bibliographic record
Abstract
Summary 1. Although diatoms have been used for many decades for river monitoring around the world, studies showing evidence that diatoms integrate temporal variability in water chemistry are scarce. 2. The purpose of this study was to evaluate the response of the Eastern Canadian Diatom Index (IDEC: Indice Diatomées de l’Est du Canada) with respect to temporal water chemistry variability using three different spatio‐temporal data sets. 3. Along a large phosphorus gradient, the IDEC was highly correlated with averaged water chemistry data. Along within‐stream phosphorus gradients, the IDEC integrated phosphorus over various periods of time, depending on the trophic status of the site studied (Boyer, Nicolet or Ste. Anne river) and variability in nutrient concentration. 4. In the Ste. Anne River, where nutrient concentrations were low and generally stable, an input of phosphorus induced a rapid change in diatom community structure and IDEC value within the following week. In the mesotrophic Nicolet River, the observed integration period was approximately 2 weeks. Diatom communities in the eutrophic Boyer River appeared to be adapted to frequent and significant fluctuations in nutrient concentrations. In this system, the IDEC therefore showed a slower response to short term fluctuations and integrated nutrient concentrations over a period of 5 weeks. 5. Our results suggest that the integration period varies as a function of trophic status and nutrient concentration variability in the streams. Oligotrophic streams are more sensitive to nutrient variations and their diatom communities are directly altered by nutrient increase, while diatom communities of eutrophic rivers are less sensitive to nutrient fluctuations and major variations take a longer time to be integrated into index values. 6. The longer integration period in the eutrophic environment may be attributed to the complexity of the diatom community. The results from this study showed that the diversity and evenness of the communities increased with trophic status.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".