Variation in Trace Metal Concentrations in A Fluvial Environment, Ottawa River, Toledo, Ohio
Bibliographic record
Abstract
Surface sediment samples were collected from a 1000 m meander reach of the Ottawa River, Ohio and analyzed for trace metals (Zn, Pb, Sr, Mn, Cu, Cr, Co, Ba, Ti, Cd, and Hg) by Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES) to determine the local variability in their concentrations between geomorphic features.Eight metals (Zn, Pb, Sr, Mn, Cu, Cr, Co, and Ba) show significantly different concentrations between five fluvial geomorphic features namely, flood plains, point bars, lateral bars, pools, and riffles.Among the features, flood plains and lateral bars are places where flows are decelerated due to surface roughness and vegetation cover, promoting the deposition of fine-grained sediments and organic matter.Thus, flood plains and lateral bars consistently exhibit the highest metal concentrations.The lowest metal concentrations in point bars are attributed to relatively coarse-grained sediment and low organic matter content.The difference in mean metal concentration between flood plains and point bars ranges from 5 times for Co to 12.5 times for Pb.It was found that Zn, Sr, Mn, Cu, Cr, Co, and Ba are influenced by similar transport and interaction processes, and possibly have common sources, while Pb shows a weak to non-significant association with other metals indicating either a different mode of transport or separate anthropogenic sources.The concentrations of all metals in 89% of the samples are below the Threshold Effect Level (TEL) and the Probable Effect Level (PEL), and thus sediment contamination issues are to be minor concern in this section of the river.The finding that flood plains serve as sediment-associated metals sink can have important implications for monitoring and regulation, impact assessment, iii and remediation of contaminated sediments in metal contaminated section of the Ottawa River or for other rivers having similar geomorphological, hydrological, and sedmentological characters.iv Dedicated to my loving parents.
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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 | 0.001 | 0.001 |
| 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".