Assessing ecosystem health impairments using a battery of ecological indicators: Bay of Quinte, Lake Ontario example
Bibliographic record
Abstract
Large freshwater and marine ecosystems suffer from a variety of anthropogenic stressors which include eutrophication, chemical contamination, coastal degradation and overexploitation of fisheries to name only a few. Attempts at remediation are often confounded by the multitude of local, regional, national and international governments and agencies that exercise jurisdiction over smaller parts of these ecosystems. In the North American Great Lakes, there exists a (nearly) 40 year track record for international cooperation in managing anthropogenic stressors that emphasizes sound ecosystem based science. Among these efforts was the designation of 42 severely polluted coastal regions as Areas of Concern (AoCs) which were deemed to have at least 1 of 14 possible Beneficial Use Impairments. The Bay of Quinte, Lake Ontario, is one AoC with 10 listed impairments. We used a “battery of tests” strategy to assess the health of the bay with respect to the impairments of “eutrophication or undesirable algae” and “degradation of phytoplankton and zooplankton communities” in the bay. This strategy integrates structural, functional and chemical parameters into established ecosystem health models. The results of the battery of tests showed continued eutrophication of the bay and not coincidentally, continued degradation of the phytoplankton community. We also found that point sources of phosphorous do not account for all of the (pelagic) primary production observed in the bay and suggest that non-point sources of phosphorous contribute significantly to eutrophication. Our results further suggest that the battery of tests strategy is a sensitive science-based tool for assessing ecosystem health. These tests could also be applied to the evaluation of ecosystem health in other Great Lakes AoCs as well as large lakes and marine environments where cultural eutrophication is a problem.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".