Developing diatom‐based inference models to assess lake ecosystem change along a gradient of metal smelting impacts: Sudbury lakes revisited
Bibliographic record
Abstract
Mining and smelting activities have strongly influenced the Sudbury region (Ontario, Canada) since the late 19th century, leading to acidification and metal contamination in many local ecosystems. Regulations on restricting acidic emissions were enacted in the 1970s, after which a considerable volume of paleolimnological work was completed to study the impacts of acidification on Sudbury‐region lakes and their subsequent biological recovery. Twenty years after the last regional diatom‐based assessment, many lakes have undergone large changes in limnological variables, including increases in pH and dissolved organic carbon concentrations, as well as decreases in metal concentrations. Additionally, these lakes are under the potential impacts of newly emerging environmental stressors such as climate warming and road salt contamination. Here, we revisited a suite of Sudbury‐region lakes (n = 80) to examine the relationships between their current water chemistry and diatom assemblages preserved in surface sediments using a canonical correspondence analysis. Although the pH gradient in our study lakes is shorter (pH ~1.4) than in earlier calibration studies conducted in this region, lake water pH was still identified as the strongest environmental variable shaping diatom distributions and was used to construct a robust inference model (R2boot = 0.73; RMSEP = 0.32). By assessing ecological changes experienced by a subset of these Sudbury‐region lakes (n = 33) over the past few decades, we identified two major trends: an overall increase in diatom‐inferred pH and a rise in the relative abundance of planktonic taxa. Our study provides useful insights into the autecology of major diatom taxa in acidified waters and highlights the importance of considering other anthropogenic stressors when assessing the recovery response of acid‐impacted systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".