Low-Nitrate-Days (LND), a Potential Indicator of Cyanobacteria Blooms in a Eutrophic Hardwater Reservoir
Bibliographic record
Abstract
Abstract When nitrate was low in a hypereutrophic, hardwater reservoir, cyanobacteria proliferated into blooms. Based on this observation an index was developed that relates an easily measurable variable, the period of Low-Nitrate-Days (LND), to the period when nuisance cyanobacteria (blue-greens) proliferate and “bloom”. The bloom indicator LND (d·yr-1) was defined as the period of time during summer and early fall when nitrate concentration is below a lake-specific threshold. This concept was valuable in Fanshawe Lake, a southern Ontario reservoir of the Thames River in the Lake Erie catchment basin, where traditional bloom indicators are rare. A nitrate threshold of 1 to 2 mg·L-1 is supported by occasional observations of chlorophyll (Chl) concentration, blue-green biomass, visual inspection, and photographic documentation. Fanshawe Lake's water quality (phosphorus, Chl, and Secchi disk transparency) varied from summer to summer and LND ranged from 0 to 175 d·yr-1 with a long-term average of 62 d·yr-1 for 38 years. LND was positively and significantly correlated with average summer total phosphorus concentration (available for 8 years), but not Chl (n = 6) nor transparency (n = 11), possibly because of an invasion by the zebra mussel Dreissena. LND values agreed well with cyanobacteria biomass indicators predicted from other models. Significant relationships with 38 years of flows and the climatic index (winter North Atlantic Oscillation) reveal that during high-flow years estimated cyanobacteria blooms are infrequent, while during low-flow years bloom periods are extended and the water quality is poor. Investigations on other man-made lakes and river sections of the Thames River, and preliminary studies on natural lakes with differing trophic states show that LND may be a useful variable in all lakes and reservoirs where nutrient limitation switches from phosphorus to nitrogen during summer.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".