Transportation of Trace Metals and Major Elements in the Ottawa River, Northwest Ohio
Bibliographic record
Abstract
Sediments in the lower parts of Ottawa River in Toledo, Ohio have a known history of contamination.Upstream studies have not shown a significant amount of contamination in sediments, but have found some metals present within the fine grained and/or organic material.This contaminated material is easily transported in the suspended load down the Ottawa River.This study addressed transport mechanisms of dissolved and solid phase to determine which was dominate for trace metal and major element concentrations.Filtered and unfiltered water samples were collected from the upper Ottawa River at 3 sites in the Wildwood Preserve Metropark (WW1, WW2, and WW3) and at 2 sites in Secor Metropark (SC2 and SC3).Samples were also collected to determine the total amount of suspended material in the river.Total Suspended Solids (TSS) was analyzed by filtering water samples through coarse, medium, and fine filter paper.Unfiltered water samples were digested following the procedure in EPA method 3105a.All water samples were analyzed for selected major and trace elements using the Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES) at Bowling Green State University.Unfiltered sample concentrations were subtracted from filtered sample concentrations to evaluate the solid phase in the suspended load.A Mann-Whitey test of the filtered and unfiltered samples showed there was a significant difference between the two sample types.Discharge was shown as the most significant factor controlling the elemental concentrations through the Principal Component Analysis (PCA) in both the unfiltered and filtered samples.The negative correlations of discharge vs. elemental iii concentrations indicate the influence of groundwater and the positive show the influence of surface water runoff.Discharge was also found to contribute to positive correlations in both the filtered and unfiltered samples for Zn and Sr.The most significant major elements contributing to the variation were Na and Ca found by unfiltered and filtered PCA.The most significant trace metals were Fe and Sr. TSS was found as not a significant factor influencing the elemental concentrations in the PCA.This result is due to the filtering process missing the grain size of 2.5µm to .45µm and grain sizes smaller than 0.45 m.Sources of the elemental concentrations can be anthropological and/or natural.Anthropological sources of overflow from adjacent storm drains could contribute to the concentrations.Natural sources of local soil and bedrock compositions within the watershed and near the metroparks can account for the elemental concentrations.Bowling Green State University.First I would like to thank my advisor, Dr. Roberts, for assisting me on my research and advising me.I also want to thank Dr. Evans and Dr. Gomezdelcampo for answering my questions and guiding me through my thesis.Thanks to those whom have helped
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".