Using zooplankton biomass size spectra to assess ecological change in a well-studied freshwater lake ecosystem: Oneida Lake, New York
Bibliographic record
Abstract
We explored the sensitivity of three descriptors of zooplankton size spectra (slope, periodic, and Pareto II models) to environmental changes in Oneida Lake, New York, and then used documented environmental changes to model the responses of zooplankton biomass using a general linear model. Using multiple regressions, we identified significant ecological events in Oneida Lake that could affect zooplankton biomass before actual model testing and assessed the three size spectrum models based on their sensitivity to these known variables. The intercept of the slope (linear regression) model was responsive to changes, but the slope was not. The periodic (quadratic) model showed no sensitivity in detecting ecological change. The Pareto II model (probability distribution function) demonstrated the most sensitivity to all ecological variables but was complex to model and there was no direct relationship between its parameters and biological events. The general linear model regression approach proved relatively sensitive to environmental change and had the added benefit of providing a graphical means of biologically assessing differences between years. In general, zooplankton biomass size spectra were responsive to changes in conditions in the Oneida Lake environment, and we believe that size-based approaches have potential as a biotic index in freshwater lake 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".