The effects of lime addition on aquatic macrophytes in hard water: <i>in situ</i> and microcosm experiments
Bibliographic record
Abstract
1. Aquatic macrophytes are abundant in ponds and canals that are constructed in semi‐arid regions for water storage and conveyance, as well as in lakes that are culturally enriched. 2. Addition of Ca(OH) 2 to two hardwater ponds at 250 or 275 mg L –1 caused an immediate eradication of submersed aquatic plants. Although these ponds are well‐buffered (alkalinity: 2.57–3.94 mequiv L –1 ; pH: 8.1–9.0), lime addition caused an immediate increase in pH of 0.2–3 units. 3. Application of 135 mg L –1 Ca(OH) 2 for 24 h or 210 mg L –1 Ca(OH) 2 for 65 h to two irrigation canals had no effect on macrophyte biomass at the lower concentration and duration, but resulted in the elimination of aquatic macrophytes 1 month after the higher concentration, longer duration treatment. 4. Unlike the macrophyte control achieved following application of 210–275 mg L –1 Ca(OH) 2 to ponds or canals, microcosm experiments in which lime formulation [slaked lime (Ca(OH) 2 ), calcite (CaCO 3 ), or a 1 : 1 mixture] and concentrations (up to 1500 mg L –1 ) were manipulated failed to elicit a consistent change in macrophyte biomass. Macrophytes in microcosms treated for the short‐term (23–33 days) with ≥ 200 mg L –1 Ca(OH) 2 or a mixed Ca(OH) 2 /CaCO 3 formulation always lost pigmentation, but biomass was not consistently reduced. 5. Declines in macrophyte biomass following treatment of ponds and canals may have been triggered by a short‐term rise in pH which, in these relatively warm (22–23 °C) alkaline (2.28–3.94 mequiv L –1 ) systems, would have resulted in low concentrations of free CO 2 and bicarbonate for photosynthesis.
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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".