Rare Earth Elements Concentration Patterns in Surface Waters of Lakes Ontario, Erie, and Huron (North America)
Bibliographic record
Abstract
The North American Great Lakes constitute a distinctive hydrological system comprising five interconnected lakes (Superior, Michigan, Huron, Erie, and Ontario) that together represent one of the planet's most significant freshwater reserves. Extensive environmental surveillance by federal, state, and provincial governments targets major water quality parameters such as temperature, pH, total dissolved solids, electrical conductivity, and dissolved oxygen, as well as concentrations of nutrients and major ions. However, trace element concentrations are more scarcely measured, and the comparatively little available data on trace element concentrations in the Great Lakes is typically older, discontinuous, or focused on historically contaminated areas. Consequently, the myriad of processes and sources involved in the distribution patterns of trace elements is poorly studied, and there remains a lack of understanding the natural baselines for these elements, including for the Rare Earth Elements (REE). The REE play a crucial role in various technological applications, including electronics, renewable energy technologies, and other high-tech industries. Because of their increasingly applications, REE are currently a significant concern, particularly in mining and industrialized areas, due to their enduring toxicity, radioactive properties, and the potential for bioaccumulation.To understand the REE distribution pattern in the North American Great Lakes, we assessed REE concentrations in >70 surface water samples from Lakes Huron, Erie, and Ontario. The concentrations of dissolved REE, filtered at
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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 | 0.001 | 0.000 |
| 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".