The Canary in the Coal Mine: Testate Amoebae Record Anthropogenic Impacts in Sediments of Oligotrophic Lake George, NY, USA
Bibliographic record
Abstract
ABSTRACT Lake George (NY) is surrounded by Forever Wild Forest in the Adirondack Park and has a Class AA Special water quality rating, yet lake monitoring has revealed increasing anthropogenic impacts from salt and nutrient loading over the past 30 years. To reconstruct anthropogenic influence on the lake (e.g., salt loading, eutrophication, climate warming), we characterized modern stable isotopes and testate amoeba and diatom assemblages in surface sediments from 33 lake-wide sites and compared their variability to 36 years of water-quality data. Linear regression analyses support testate amoebae as rapid responders and recorders of environmental change because taxa are strongly correlated with percent change of important water quality parameters. Our assessment indicates that: 1) Netzelia gramen is associated with aquatic plants and filamentous algae, making them a valuable aquatic plant/alga indicator, which is supported by the co-occurrence of the diatom Cocconeis spp.; 2) difflugids are generally good indicators of eutrophication, except for Difflugia protaeiformis; and 3) seasonal differences in water quality trends are reflected in the fossil record on decadal time scales. We show that testate amoebae are highly sensitive to small environmental changes in an oligotrophic lake and exhibit established relationships from eutrophic and mesotrophic lakes as well as new, likely oligotrophic-specific correlations. Correlation coefficients of water quality variables and strains within a species also illustrate gradational relationships, suggesting testate amoebae exhibit ecophenotypic plasticity. Diatom and testate amoeba assemblages categorize modern lakebed sites into four subgroups: 1) benthic macrophyte; 2) high nutrient; 3) high alkalinity; and 4) salt loading assemblages.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".