Long-term directional trajectories among lake crustacean zooplankton communities and water chemistry
Bibliographic record
Abstract
Understanding community responses to disturbance is a long-standing challenge in ecology and will remain a critical issue as human activity continues to alter environmental conditions. This is particularly concerning for freshwater communities, which are often subject to the effects of multiple disturbances on the landscape. With crustacean zooplankton community composition data and associated water chemistry from 19 Ontario lakes, we quantify the relative magnitude and directionality of change over three decades using distances in multivariate ordinations. The data span a gradient of impact from minimally disturbed reference lakes to lakes with known impacts from experimental acidification trials, atmospheric acidification, and regional land-use changes. Most communities exhibited changes from historical conditions, including previously described reference systems. Zooplankton communities in experimentally acidified lakes showed gradual, directional patterns with a return to historical conditions postmanipulation, but have since deviated again from historical conditions. Most zooplankton communities in atmospherically acidified lakes showed gradual, directional trajectories over time. Overall, our results demonstrate that zooplankton communities are changing among both minimally disturbed lake systems and lakes known to be impacted by human activities.
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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 | 0.001 |
| 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 | 0.000 |
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".