BIOMONITORING SITE QUALITY IN STRESSED AQUATIC ECOSYSTEMS USING<i>VALLISNERIA AMERICANA</i>
Bibliographic record
Abstract
Leaf-to-root surface area ratios in Vallisneria americana have been shown to provide a simple and inexpensive relative measure of sublethal effects of organochlorine contamination. The present study was conducted to determine whether this index of surface area could be used as an effective biomonitor of overall site quality in stressed aquatic ecosystems. The leaf-to-root surface area ratio was determined for samples of V. americana collected from 225 microsites within 12 Areas of Concern (environmentally contaminated areas designated by the International Joint Commission) throughout the Laurentian Great Lakes of Ontario. Statistical analyses indicate that 77% of the variation in the surface area index could be attributed to differences among microsites, with only 23% of variation occurring among plants within a microsite. A multiple regression equation was developed for predicting the leaf-to-root surface area ratio from several measures of microsite quality. Significant parameters affecting the surface area ratio included plant density, light intensity, and an index of sediment contamination. In contrast, measures of water contamination did not show any correlation with leaf-to-root surface area ratio. These observations support the hypothesis that V. americana accumulates contaminants primarily from the sediments and that the leaf-to-root surface area ratio can be used to construct contours of point source impact zones in Areas of Concern. The regression model developed here provides a simple, inexpensive means for monitoring overall site quality throughout the Great Lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".