Effect of a moderate-size reservoir on transport of trace elements in a watershed
Bibliographic record
Abstract
Wildman RA, Forde NA. 2016. Effect of a moderate-size reservoir on transport of trace elements in a watershed. Lake Reserve Manage. 32:353–365.We assessed the extent to which Grand Lake, Oklahoma (>30 m deep, >80 km long), retains Fe, Mn, P, As, Zn, Pb, and Cd. Filtered water samples and suspended sediment samples were collected upstream of, within, and downstream of the reservoir. We then estimated instantaneous, seasonal, elemental fluxes. In winter and spring, when storms brought high flows to the reservoir, Grand Lake modified flood water minimally. During these seasons, trace element distributions were determined by the passage of storm inflows through the reservoir. In summer, Fe, Mn, P, and As were enriched in anoxic bottom water and exported through the dam, which draws water from below the surface mixed layer. Concentrations of aqueous elements in the water column were lower following autumn overturn, perhaps due to precipitation of metal oxides and settling. Unlike Fe, Zn was retained in Grand Lake during all seasons. Concentrations of Cd and Pb in filtered samples were often below our detection limit. Logistic regression indicated that Zn predicted detectable Cd, and so Grand Lake probably sequesters Cd. Sequestration of Pb was unclear because detectable Pb was predicted by both Zn and Fe. This study shows that watershed hydrology determines the transport of trace elements through a reservoir during times of high flow but that vertical circulation and biogeochemistry dominate during summertime and autumn low flows. Understanding these mechanisms can aid reservoir managers who seek to reduce downstream loads of trace elements.
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 | 0.001 | 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) | 0.001 | 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 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".