Rare earth element distribution patterns in Lakes Huron, Erie, and Ontario
Bibliographic record
Abstract
Rare earth elements (REE) are increasingly used in industrial applications, consumer electronics and green technologies, but their baseline concentrations and distribution patterns in the North American Great Lakes remain poorly understood. Here, we report dissolved REE concentrations in > 70 surface water samples from Lakes Huron, Erie, and Ontario (2021 and 2022) and assess their spatial distribution patterns and governing biogeochemical controls. Dissolved (<0.22 µm-filtered) REE concentrations were spatially heterogeneous (up to 3 orders-of-magnitude) across the lakes and did not systematically increase upstream-to-downstream through the basin. Nearshore-to-offshore decreases in dissolved REE levels were observed for all lakes and appeared the result of REE adsorption to colloids and subsequent sedimentation. Combined with enrichment of light over heavy REE, particularly in samples closer to shore, our data suggests that riverine input is a major pathway by which REE are loaded to the lakes. Finally, we used normalization and pattern-filling to assess REE anomalies in the lake surface waters. Anomalies for Gd (>20 % across the lakes) were notably higher than those of the other REE but varied significantly spatially, and also showed enrichment nearshore, particularly near urban centers and in Lake Ontario. This work provides new surveillance data to further develop our understanding of REE dynamics in the Great Lakes.
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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) | 0.000 | 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) | 0.004 | 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".