Scaled chrysophytes as indicators of water quality changes since preindustrial times in the MuskokaHaliburton region, Ontario, Canada
Bibliographic record
Abstract
Scaled chrysophytes preserved in modern and fossil sediment samples from lakes in south-central Ontario were examined to evaluate changes in water quality since preindustrial times. Redundancy analysis determined that chrysophyte distributions were related to a primary gradient of pH, alkalinity, and ion concentration (λ1 = 0.26). A 117-lake reconstruction model from Ontario, the Adirondacks, and northeastern U.S.A. was used to infer the lakewater pH of present-day and preindustrial samples. A comparison of predicted and measured pH values of modern samples, analog matching, and an examination of inferences from triplicate cores in four lakes suggested that the inferences were reliable. Reconstructions indicated that presently acidic lakes (pH < 6) had acidified, whereas lakes with measured pH > 7 had become more alkaline. In comparison to other acid-sensitive regions, however, the overall change was small. The relatively short pH gradient, higher preindustrial pH values, and amount of acid deposition are factors that may explain these trends. Finally, we introduce a novel, multi-indicator reconstruction model, which provides an average of environmental reconstructions from diatom, chrysophyte cyst, and scaled chrysophyte assemblages. This model performed as well or better than the individual inferences when used to predict the pH of modern samples.
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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 | 0.001 | 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".