DEVELOPMENT, TESTING, AND APPLICATION OF STRESSOR GRADIENTS IN RURAL, HEADWATER STREAMS IN SOUTHWESTERN ONTARIO
Bibliographic record
Abstract
Effective biological monitoring requires a conceptual model of how human activity varies and how this activity affects resident biota. This model can then be used to generate appropriate hypotheses and study designs in response to bioassessment needs. Stressor gradients have the potential to improve this process, but questions about the development and effectiveness of stressor gradients must be addressed before they can be widely applied to biological monitoring.\nWith the aim of developing the most effective and efficient stressor gradient, four gradients were calculated from stressor information differing in level of detail and spatial explicitness for 479 rural, headwater basins. Fine detail gradients also described substantially more variation in the stressor environment than those using coarse detail data. Data that described the location of the stressors within the basin resulted in only minimal improvements to the description of the stressor environment.\nThe responsiveness of aquatic biota to stressor gradients was determined using surveys of aquatic assemblages in 160 small, rural, streams. Canonical correspondence analysis indicated that fish and macroinvertebrates responded to stressor gradients through compositional shifts from intolerant to tolerant taxa as human activity intensified. This response was confounded by a similar compositional shift in response to a gradient of surface geology. Partial Mantel’s tests controlling for the effect of natural gradients indicated that aquatic assemblages are associated to gradients in the human environment.\nA stressor gradient was applied in the development of an objective method for selecting environmentally stratified, regional reference sites for the purpose of assessing ecological condition in freshwater ecosystems. This method groups potential sites based on their natural environments prior to establishing the degree of human activities occurring at each site within each group. Sites exhibiting the least amount of human activity are then selected to act as reference sites for each group.\nIn addition to having immediate impact on how biological monitoring is conducted in the Southwestern Ontario region, the results of this study can be conceptually applied to bioassessments worldwide. Furthermore, this study can act as the\niii\nfoundation for using stressor gradients for the development of predictive models that will aid in planning and management of future activities that may affect aquatic ecosystems.
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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 | 0.003 |
| 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) | 0.000 | 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".