Comparisons of zooplankton community size structure in the Great Lakes
Bibliographic record
Abstract
Zooplankton mean size and size spectra distribution potentially reflect the condition of trophic interactions and ecosystem health because they are affected by both resource availability and planktivore pressure. We assessed zooplankton mean size and size spectra using an optical plankton counter (OPC) on 35 site visits among lakes Superior, Michigan, Huron, Erie, and Ontario (2002–2003). The surveys were conducted in both nearshore regions (5–20 m depth) and on associated transects to offshore regions either greater than 8 km from shore or greater than 100 m depth. The survey sites were distributed across a gradient of land use disturbance in watersheds adjacent to the nearshore regions. The mean size, biomass density, statistical size distribution, and normalized biomass size spectra of zooplankton were determined from OPC measurements for all locations. Significant differences among lakes were observed in mean size, biomass, and the parameters of size spectra distributions for both nearshore and offshore regions. Significant differences within lakes were observed in some parameters that also allowed for significant discrimination between nearshore and offshore zooplankton communities in lakes Michigan (mean size, biomass, one spectral parameter), Ontario (mean size, three spectral parameters), and Superior (one spectral parameter). Similarly, some parameters allowed for discrimination between offshore epilimnion and hypolimnion waters in lakes Michigan (mean size, biomass, and four spectral parameters), Huron (biomass), and Ontario (two spectral parameters). The use of OPC technology and parameters that characterize spectral shape may have potential as an efficient and economic way for developing a size‐based zooplankton metric to discriminate among zooplankton communities in the Great Lakes.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".