Distribution and flux of microcystin congeners in lake sediments
Bibliographic record
Abstract
Zastepa A, Pick FR, Blais JM. 2017. Distribution and flux of microcystin congeners in lake sediments. Lake Reserv Manage. 33:444–451.Sediment concentrations of microcystin congeners and exchange across the sediment–water interface were determined in Lake of the Woods, a large water body between Canada and the United States experiencing cyanobacterial blooms. Dated sediment cores were used to examine historical occurrence of microcystins and showed that microcystins were below detection prior to the 2000s. In more recent sediments the most abundant congeners were MC-LA and -LR with -RR, -YR, -7dmLR, -WR, -LF, -LY, and -LW also present. MC-LA and -LR were also distributed in the pore waters whereas MC-RR and -YR were more strongly adsorbed to sediment particles. Sediment burial rates for MC-LA and -LR were determined from the product of the microcystin concentration on sediment particles (ng/g dw) and the burial rate (based on 210Pb radiochronology [g/m2/d]). Diffusion from sediments was estimated from the concentration gradient between pore water of surficial sediments and overlying water using Fick's first law. Overall, burial rates were low across sites (2.6 to 298.1 ng/m2/d) when compared to diffusion of microcystins from sediments to overlying water (303.1 to 1078.0 ng/m2/d) suggesting that sediments can be a source of microcystins to the water column. However, the relatively high diffusive flux may be short term and the result of a temporal disconnect between water column productivity and sediment processes. The higher diffusion fluxes and lower burial rates of MC-LA compared to MC-LR point to differences in environmental fate. Given that microcystin congeners vary in their toxicity, these results highlight the need for congener-specific measurements of environmental fate and persistence.
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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 | 0.002 | 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) | 0.001 | 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".