An Assessment of Environmental Changes in Three Lakes from King's County (Nova Scotia, Canada) Using Diatom-Based Paleolimnological Techniques
Bibliographic record
Abstract
Abstract Paleolimnological approaches using sedimentary diatom assemblages were used to assess water quality changes over the last approximately 200 years in three lakes from King's County, Nova Scotia. In particular, the role of recent shoreline development in accelerating eutrophication in these systems was assessed. Sediment cores collected from each lake were analyzed for their diatom assemblages at approximately 5-year intervals, as determined by 210Pb dating. Analyses showed that each system has changed, but tracked different ecosystem changes. Tupper and George lakes recorded shifts, which are likely primarily related to climatic warming, with diatom assemblages changing from a preindustrial dominance by Aulacoseira spp. to present-day dominance by Cyclotella stelligera. In addition to the recent climatic-related changes, further diatom changes in the Tupper Lake core between approximately 1820 and 1970 were coincident with watershed disturbances (farming, forestry, and construction of hydroelectric power infrastructure). Black River Lake has recorded an increase in diatom-inferred total phosphorus since about 1950, likely due to impoundment of the Black River system for hydroelectric generation and subsequent changes in land runoff. Before-and-after (i.e., top-bottom) sediment analyses of six other lakes from King's County provided further evidence that the region is being influenced by climatic change (decreases in Aulacoseira spp., increases in planktonic diatom taxa), as well as showing other environmental stressors (e.g., acidification). However, we recorded no marked increase in diatom-inferred nutrient levels coincident with shoreline cottage development in any of the nine study lakes. Paleolimnological studies such as these allow lake managers to place the current limnological conditions into a long-term context, and thereby provide important background data for effective lake management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".