Assessing pH changes since pre-industrial times in 51 low-alkalinity lakes in Nova Scotia, Canada
Bibliographic record
Abstract
Diatom-based paleolimnological techniques were used to reconstruct lake acidification trends in 51 low-alkalinity Nova Scotia lakes that spanned gradients of dissolved organic carbon (DOC) concentrations and sulphate deposition. Pre-industrial, diatom-inferred pH values of these lakes were <6.8, with 31 lakes having pre-industrial pH < 6.0 and two lakes having pH < 5.5. Lakes in Kejimkujik National Park documented the greatest pH decline (–0.4 pH unit (±0.2)) since the 19th century, whereas those in northern parts of the province (e.g., Cape Breton Highlands National Park) experienced little or no acidification, with a net mean pH decline = –0.1 pH unit (±0.2). While the sulphate deposition and diatom-inferred pH changes have not been as great as those observed in other acidified areas of northeastern North America (e.g., Adirondack region of New York or New England), Nova Scotia lakes have experienced biological changes toward more acidophilous diatom assemblages, especially in lakes with low pre-industrial pH values (currently with high DOC concentrations) located in Kejimkujik National Park, which receives the highest loading of sulphate deposition in Nova Scotia. However, the generally low pre-industrial pH values inferred for most of the study lakes suggest that many of these lakes were somewhat naturally acidic, but acidified further as a result of atmospheric deposition.
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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 | 0.002 |
| 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".