Impact of Ca(OH)<sub>2</sub> treatment on macroinvertebrate communities in eutrophic hardwater lakes in the Boreal Plain region of Alberta: in situ and laboratory experiments
Bibliographic record
Abstract
Ca(OH)2 treatment has been recently used to remediate water quality problems associated with eutrophication in lakes on the Boreal Plain of western Canada. This study examines how Ca(OH)2 treatment affects the survival of Hyalella azteca and larvae of Chironomus spp. in laboratory microcosms and the abundance and seasonal dynamics of macroinvertebrates in two eutrophic hardwater lakes. Macroinvertebrate data for three reference lakes sampled during the same time period are also presented. Additions of 225 and 295 g Ca(OH)2·m-2 (74 and 107 mg·L-1) to two lakes resulted in lake water pH remaining within its natural range (<10) and had no discernable effects on macroinvertebrate density or biomass up to 2 years after treatment. Macroinvertebrate response to Ca(OH)2 in hardwater lakes differed markedly from results reported for softwater lakes. After 4 days, no detectable influence was observed on survivorship of Chironomus spp. with laboratory dosages of 0-300 g Ca(OH)2·m-2 (0-1781 mg·L-1), but survivorship of H. azteca was reduced by >= 50% at dosages that raised water pH to >=10 (300 g Ca(OH)2·m-2). The sensitivity of H. azteca, but not Chironomus spp., could be a result of singular or combined effects of Ca(OH)2 levels, precipitate, or changes in pH. It may also reflect the greater dependence of H. azteca than Chironomus spp on the water above, rather than below, the sediment-water interface.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".