Water Quality Changes from Human Activities in Three Northeastern USA Lakes
Bibliographic record
Abstract
Diatom and chrysophyte assemblages from sediment cores were analyzed to assess the long-term trends of lake water quality in French Pond (New Hampshire), Joes Pond (Vermont), and Kenoza Lake (Massachusetts) as part of the US EPA's EMAP-SW (Environmental Monitoring and Assessment Program-Surface Waters) program in the northeastern USA. Sediment characteristics and geochemical data were also examined to interpret past limnological and watershed changes. Geochemical data indicate that exports of ions from the watersheds have increased and the lakes have received higher trace metal inputs over the post-industrial period. Stratigraphic changes in common diatom and chrysophyte taxa indicate that, over the last century, distinct water quality changes have occurred. Using the diatom- and chrysophyte-based weighted averaging inference models developed for lakes in the northeast, past changes in assemblages were used to infer trends in lakewater total phosphorus (TP), pH, and CI. In French Pond, inferred TP, pH, and CI changes were small, whereas Joes Pond and Kenoza Lake have experienced major changes. The latter two lakes have become more eutrophic, and lakewater pH and CI have also increased from their background values. Inferred water quality changes are closely related to watershed disturbances and resulting eutrophication. Our study illustrates that the inference models developed in EMAP-SW can be successfully applied in establishing long-term water quality trends in lakes throughout the northeastern USA. These models and subsequent sediment core data will help lake managers to develop effective management plans and to establish suitable targets for the restoration of other lakes of concern.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".