Calibration of the zooplankton community size spectrum as an indicator of change in Canadian Shield lakes
Bibliographic record
Abstract
Developing the crustacean zooplankton community size spectrum into an indicator of change in lakes requires quantification of the natural variability in the size spectrum related to broad-scale seasonal, annual, and spatial factors. Characterizing seasonal patterns of variation in the size spectrum is necessary so that monitoring programs can be designed to minimize the masking effects that seasonal processes can have on detecting longer-term temporal change. We used a random effects model to measure monthly, annual, and interlake variability in the slope (i.e., relative abundance of small and large organisms) and centered height (i.e., total abundance) of the crustacean zooplankton normalized abundance size spectrum from 1981 to 2011 among eight Canadian Shield lakes. Consistent with theoretical predictions, the slope was a relatively stable characteristic of the zooplankton community compared with the height, which varied significantly among lakes. We identified a seasonal signal in height and slope and used a mixed effects model to characterize the linear rate of change from May to October; there was an overall decline in height and an overall increase in slope. Seasonal variance was greater than annual variance for both the height and the slope, suggesting that long-term monitoring of lakes and interlake comparisons using zooplankton size spectra should be based on temporally standardized sampling protocols that minimize the effects of seasonal processes. We recommend sampling the zooplankton community in midsummer because this results in size spectrum estimates close to seasonal mean values.
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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".