Spatiotemporal and multi-isotope assessment of metal sedimentation in the Great Lakes
Bibliographic record
Abstract
This study investigates spatiotemporal dynamics in metal sedimentation in the North American Great Lakes and their underlying biogeochemical controls. Bulk geochemical and isotope analyses of n=72 surface and core sediment samples show that metal (Cu, Zn, Pb) concentrations and their isotopic compositions vary spatially across oligotrophic to mesotrophic settings, with intra-lake heterogeneity being similar or higher than inter-lake (basin-scale) variability. Concentrations of Cu, Zn, and Pb in sediments from Lake Huron and Lake Erie vary from 5 to 73 mg/kg, 18 to 580 mg/kg, and 5 to 168 mg/kg, respectively, but metal enrichment factors were small (<2) across the surface- and core sediments. The isotopic signatures of surface sediment Cu (δ65Cu between −1.19‰ and +0.96‰), Zn (δ66Zn between −0.09‰ and +0.41‰) and Pb (206/207Pb from 1.200 to 1.263) indicate predominantly lithogenic metal sourcing. In addition, temporal trends in sediment cores from Lake Huron and Lake Erie show uniform metal concentrations, minor enrichment, and Zn and Pb isotopic signatures suggestive of negligible in-lake biogeochemical fractionation. In contrast, Cu isotopic signatures and correlation to chlorophyll and macronutrient levels suggest more differentiation from source variability and/or redox-dependent fractionation, likely related to biological scavenging. Our results are used to derive baseline metal sedimentation fluxes and will help optimize water quality management and strategies for reducing metal loads and enrichment in the Great Lakes and beyond.
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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 | 0.001 |
| 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) | 0.000 | 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".