Lime treatment and its effects on the chemistry and biota of hardwater eutrophic lakes
Bibliographic record
Abstract
1. The main focus of this study was to investigate the effects of single and multiple moderate doses of lime (slaked lime, Ca(OH) 2 , and/or calcite, CaCO 3 ) on eutrophic hardwater lakes. This information would contribute to strategies to manage phytoplankton and macrophyte biomass in eutrophic lakes. 2. Water chemistry and biota were monitored for up to 7 years after initial lime treatment and results were compared with reference systems. 3. Complementary studies investigated the effect of lime on macrophytes in ponds, irrigation canals and microcosm experiments. 4. When water pH was kept within its natural range (≤ 10), single and multiple lime applications to lakes and ponds controlled macrophyte biomass, without negatively affecting invertebrate communities. 5. Single lime treatments at moderate dosages of lakes and ponds resulted in variable and mostly temporary changes in chlorophyll a (chl a ) and phosphorus (P) concentration. Although sediment P release was reduced in single‐dose lakes during the first winter following treatment, reductions appeared temporary. 6. Multiple treatments of lakes and ponds were effective at reducing both chl a and P concentrations over longer periods. Mean winter P release rate was also reduced after initial treatment. 7. In laboratory studies, sediment cores were incubated with eight different treatments to assess P release. Redox‐sensitive treatments were no more effective at lowering total P concentration in overlying water than some redox‐insensitive treatments. Lime reduced total P concentrations, but was not as effective as treatments with alum. 8. The use of lime in managing macrophyte and phytoplankton biomass in shallow, hardwater lakes and ponds may be preferable over other treatments, because lime is economical and non‐toxic as long as pH is kept within a natural range.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".