A Systems Analysis of Trace Element Cycling in the Great Lakes
Bibliographic record
Abstract
The Great Lakes are a globally unique freshwater resource, comprising six lakes (Ontario, Erie, Huron, Superior, Michigan, Lake St. Clair) containing 20% of global surface freshwater resources. The region is known for manufacturing and agriculture; anthropogenic waste steams (industrial effluents, agriculture runoff) impact occurrence patterns and natural cycles of various chemical elements, however current baseline concentrations remain unknown and biogeochemical controls underlying their sources, mobility, and fate remain unknown. This thesis utilizes an environmental systems approach to investigate three topics: 1) accumulation dynamics in trace element loads across the hydrological components of the Great Lakes system, 2) investigating the Great Lakes system as a ‘black-box‘ system for analyzing conservative and more biogeochemically reactive element budgets, and 3) anthropogenic controls on trace element budgets via geospatial and speciation analysis. Quantitative long-term mass-balances were constructed for a selection of important metals (Ni, Cu, Zn, Pb) and conservative elements (Na, Cl), the latter of which achieved >90% closure. Dynamic simulations and potential element sources/sinks were assessed, and historical water quality trends reproduced. Future water quality trends were simulated to year 2100 under varying environmental scenarios, and differing degrees of accumulation or reduction were observed. Novel trace element data was subsequently interpreted using mass-balance assessments. Different trace elements displayed significant accumulation upstream-to downstream, however trace elements Li, As, Se, V, Cr, Ce, and Gd displayed varying patterns of accumulation. These elements displayed differing sources and transport: connecting channels dominate trace elements budgets of the lower lakes, whereas atmospheric inputs are significant in the upper lakes. Spatial heterogeneity in trace element concentrations was observed, and speciation analysis revealed consistent proportions of trace element species upstream-to-downstream. Overall trace elements dynamics across the basin are spatiotemporally variable, accumulation patterns vary between elemental groups, and elemental controls differ in the upper versus lower lakes. The ‘black-box’ methodology proved effective for determining quantitative mass-balance dynamics for conservative trace elements, and high uncertainty in concentrations for other trace elements produced higher spatiotemporally variability and mass-imbalances. As such, further long-term basin-scale monitoring is required to properly constrain the relative magnitudes of the factors affecting these trace element budgets (e.g., tributaries, sedimentation).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".