pH 6 as the threshold to use in critical load modeling for zooplankton community change with acidification in lakes of south-central Ontario: accounting for morphometry and geography
Bibliographic record
Abstract
Identifying thresholds of biotic community change along stressor gradients may be useful to both ecologists and lake managers; however, there are several weaknesses in the thresholds that have been identified for zooplankton communities along acidity gradients. The thresholds are often based on a single species even though pH sensitivities vary among species. They often measure changes in species occurrences, though abundances may be a more responsive indicator of damage. Their identification may be confounded by spatial and morphometric factors if they are derived from lake surveys. Finally, the thresholds have usually been subjectively identified. Our goal was to establish a threshold in zooplankton community change along an acidity gradient that did not have these four common weaknesses. We used two crustacean zooplankton community metrics: species richness and scores of a correspondence analysis based on species abundances. Spatial and morphometric patterns were detected in the zooplankton community data and then extracted. The relationship between zooplankton and acidity was then modeled using a step function that objectively identified a threshold of community change at pH 6 for lakes in south-central Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".