Spatial and temporal patterns in microcystin toxins in Lake of the Woods surface waters
Bibliographic record
Abstract
Zastepa A, Watson SB, Kling H, Kotak B. 2017. Spatial and temporal patterns in microcystin toxins in Lake of the Woods surface waters. Lake Reserve Manag. 33:433–443.Lake of the Woods (LOW) is a complex system, with limited exchange between its multiple basins and strong spatial variance in physicochemical conditions and susceptibility to cyanobacterial blooms (cHABs). Nutrient input from tributaries has contributed to a highly productive southern basin with widespread cHABs, which are also present in some northern sub-basins. To date, there is no systematic lake-wide assessment of spatial and temporal patterns of microcystins (MCs) in relation to cyanobacterial taxa along physicochemical gradients. To achieve this, surface waters were sampled June to September in 2006, 2008–2010, and 2014–2015 offshore, inshore, and along developed shorelines. Offshore MC measured by ELISA and PPIA was consistently low despite high chlorophyll-a (Chl-a) (e.g. 0.7 µg MC-LR equivalents/L; 126 µg/L Chl-a), with only two cases exceeding the 20 µg MC-LR equivalents/L World Health Organization recreational guideline. LC-MS/MS analysis of MC congeners (2014–2015) showed MC-LR dominating, with MC-LA also present. A strong positive correlation of MC with NO3 (P < 0.005) was observed but not with other nutrients, water transparency, or mixing. The toxicity of phytoplankton biomass was positively correlated with dissolved P and inversely with particulate N:P (P < 0.02, P < 0.001 respectively), suggesting lower toxicity under P-deficiency. Shorelines contained much higher MC concentrations with more than 25% above 20 µg MC-LR equivalents/L in 2006 and 2009 (maximum ∼ 600 µg MC-LR equivalents/L). Several potential producers of MCs were suspected, including Dolichospermum and Microcystis. Cyanobacteria identified in LOW are known elsewhere as producers of other toxins including neurotoxins, but these have not yet been assessed in LOW.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".