Submerged macrophytes, zooplankton and the predominance of low‐ over high‐chlorophyll states in western boreal, shallow‐water wetlands
Bibliographic record
Abstract
Summary 1. Shallow‐water (<2 m deep) wetlands in northern Alberta's western boreal forest tend to have either extremely high‐chlorophyll, pea‐green water or water that is low in chlorophyll and clear. The relative importance of top‐down processes (e.g. zooplankton grazing) compared with bottom‐up processes (e.g. nutrient limitation) for regulating the existence of these alternate states has yet to be explicitly investigated in these poorly studied waterbodies. 2. To assess the relationship between chemical and biological factors and the predominance of low‐chlorophyll over high‐chlorophyll states, a survey of 24 shallow‐water wetlands in northern Alberta was conducted over the summers of 2001 and 2002. 3. In wetlands without fish, statistical analysis indicated that high‐ and low‐chlorophyll sites could be differentiated with 82% accuracy based solely on whether submerged aquatic vegetation (SAV) coverage was greater or less than 25%. Further, high‐SAV lakes with zooplankton communities dominated by large cladocerans were clear 96% of the time. 4. In the few wetlands that supported stickleback populations, large cladocerans were absent. However, the development of zooplankton communities dominated by small cladocera (Bosmina) with calanoid copepods in fish‐containing wetlands corresponded with a shift from high‐ to low‐chlorophyll states. 5. Results suggest that in fishless wetlands high‐SAV coverage and grazing by large, SAV‐associated cladocerans promotes low‐chlorophyll states. Results also suggest that suppression of macroinvertebrate predators by sticklebacks allowing Bosmina to flourish may also promote low‐chlorophyll, clear‐water conditions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".