Monitoring periphyton in lakes experiencing shoreline development
Bibliographic record
Abstract
Early detection of degradation is crucial in previously pristine lakes experiencing residential development along their shores. Despite suggestions that the littoral zone responds to anthropogenic disturbance before open water, the use of periphyton for monitoring lake trophic status has been hindered by the heterogeneous distribution of this community. We examined the response of periphyton growing on different natural substrata — rocks, wood, sediments, and macrophytes — as well as on introduced plastic strips along a gradient of residential development in the Laurentian lakes (Quebec). We measured periphyton biomass as chlorophyll a and as thickness estimated with a ruler with the goal to evaluate the best method to monitor the incipient degradation of these lakes. Our findings suggest that rocks are the best substratum to sample because they are ubiquitous, and epilithic algae show a stronger response to shoreline residential development than algae on other substrata. Measurement of epilithon thickness appears a fast and reliable tool for estimating epilithon biomass. If measurements of chlorophyll a require several field and laboratory manipulations that are not readily available for voluntary lake monitoring by residents, measurement of periphyton thickness on rocks may allow examining spatial and temporal changes in a large number of lakes.
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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 | 0.000 |
| 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".