Periphyton as an early indicator of perturbation in recreational lakes
Bibliographic record
Abstract
We looked for empirical relationships between periphyton biomass and recreational development in the Laurentian lakes of Quebec (Canada). We compared the response of periphyton (as chlorophyll a) on rocks and sediments with that of phytoplankton. Epilithon and epipelon biomass increased significantly with lake recreational development (as percentage of cleared land within a 50 m riparian strip) but was not related with open-water phosphorus concentration. In contrast, phytoplankton was related to open-water phosphorus but did not increase along the gradient of lake development. Periphyton stoichiometric composition also changed with increasing lake development. High C:P and C:N ratios were found in pristine lakes, whereas lower periphyton molar ratios, which approached the optimal stoichiometric composition for benthic microalgae, were observed in the most developed lakes. Our findings suggest that periphyton, positioned near the land–water interface, has access to land-derived nutrients before they are diluted in the open water. Therefore, periphyton on all substrata is the first community to respond to increased inputs resulting from lake recreational development. The measurement of littoral algal biomass and chemical composition may represent a better tool for early detection of lake perturbation than classic methods based on pelagic characteristics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".