Diatom-inferred decline of macrophyte abundance in lakes of southern Quebec, Canada
Bibliographic record
Abstract
Submerged macrophytes play a central role in lake ecosystem functioning; however, their long-term dynamics are poorly understood. We have used the paleolimnological top–bottom approach to reconstruct changes in whole-lake macrophyte biomass between predisturbance and present-day conditions in 37 southern Quebec lakes. Estimates of whole-lake macrophyte biomass were produced using a diatom-based multivariate regression tree model (MRT) and modern analogue approach. Both approaches indicated an overall pattern of declining macrophyte abundance in the region. Based on MRT analysis, 80% of study lakes were classified as historically being in the macrophyte-dominated state, but now only 43% of the lakes are currently in this state. The lakes that shifted MRT group were found to have significantly (p = 0.03) greater building densities in their catchments compared with lakes that did not shift state. These results suggest that human impacts, primarily nutrient inputs and water level fluctuations, have played a role in reducing macrophyte abundance in southern Quebec lakes. Because submerged macrophyte beds help stabilize lake ecosystems and act as a phosphorus sink, a reduction in whole-lake macrophyte biomass could make lake ecosystems more susceptible to eutrophication.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".