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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".