The role of flooding on inter‐annual and seasonal variability of lake water chemistry, phytoplankton diatom communities and macrophyte biomass in the Slave River Delta (Northwest Territories, Canada)
Bibliographic record
Abstract
Abstract Water chemistry, macrophyte biomass and planktonic diatom communities were monitored seasonally over 3 years (2003–2005) from six hydrologically diverse lakes to assess the role of river flooding on inter‐annual and seasonal variability of conditions in lakes of the Slave River Delta, Northwest Territories, Canada. Results indicate that river flooding was the dominant hydrological process controlling the temporal dynamics of the physical and chemical conditions, planktonic diatom communities and macrophyte biomass in lakes of the Slave River Delta. In the absence of river flooding, lakes had relatively high concentrations of nutrients and low concentrations of most ions, but when flooded, concentrations of nutrients decreased and ions increased. The physical and chemical conditions in frequently flooded and non‐flooded lakes were relatively stable from year to year, whereas lakes that were intermittently flooded fluctuated widely depending on whether or not they flooded. Spring flooding from the Slave River introduced planktonic, centric diatoms that persisted only for a few weeks in the water column before settling out. Non‐flooded lakes lacked planktonic diatoms. River flooding also reduced water transparency, which decreased macrophyte biomass, while lakes that did not flood exhibited higher macrophyte biomass and clear waters. This research provides insights into the factors that control the hydroecological variability of northern deltaic landscapes, and further improves our understanding of the complex interactions among hydrology, limnology and aquatic ecology, ultimately contributing to an improved scientific basis for future resource management decisions in the Slave River Delta and analogous systems. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".