Twentieth century water quality trends in Minnesota lakes compared with presettlement variability
Bibliographic record
Abstract
A diatom-based transfer function was used to reconstruct water chemistry before European settlement in 55 Minnesota lakes. The lakes span three natural ecoregions, which differ in their history of land use, as well as in surficial geology, climate, and vegetation. Postsettlement trends were compared with water chemistry change reconstructed from two presettlement core sections (circa 1750 and 1800) as a measure of natural variability. Presettlement water quality changes were generally small and nondirectional in all three ecoregions. In contrast, half of the urban lakes showed a statistically significant increase in chloride, whereas 30% of urban and 30% of agricultural region lakes record a statistically significant increase in total phosphorus between 1800 and the present. These changes, which are attributed to road salt and nutrient runoff, are strongly correlated with the percentage of watershed area that is developed (residential or urban) in the case of chloride increases and the percentage of developed (metropolitan areas) or agricultural (agricultural areas) land in the case of nutrient increases. Water quality has changed little since 1800 for lakes in the forested regions of northeastern Minnesota. The few changes that are seen in this region are likely related to natural variations in climate or catchment soils.
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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 | 0.000 |
| 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) | 0.000 | 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".