Correspondence among methods of zooplankton biomass measurement in lakes: effect of community composition on optical plankton counter and size-fractionated seston data
Bibliographic record
Abstract
The effectiveness of the optical particle counter (OPC) to estimate zooplankton biomass depends on the variability in zooplankton shape and the presence of interfering particles. In marine environments where zooplankton are composed of similarly shaped copepods, an average shape is relatively easily obtained. However, in freshwater environments, spheroid cladocerans mix with ellipsoid copepods and make the application of a single morphometric model difficult. To expand the use of the OPC to freshwater environments, we developed new ellipsoid models for three common lake types (eutrophic, mesotrophic, and oligotrophic). In addition, we assessed how closely different size fractions of seston corresponded to zooplankton biomass. When expressed in common dry mass units, OPC- and seston-derived zooplankton biomass estimates showed a 1:1 correspondence with taxonomically derived estimates in productive lakes (r > +0.70, P < 0.001) but not in oligotrophic systems. OPC ellipse models differed among lake sets (major-to-minor axis ratio: 1.5 to 2.7) but were not a simple function of the cladoceran-to-copepod ratio. The seston size fraction that provided the best estimates of zooplankton biomass was smaller in mesotrophic lakes (>200 μm) than in eutrophic or oligotrophic lakes (>500 μm). The presence of algae and rotifers had no detectable influence on OPC and size-fractionated seston estimates. Overall, these analyses suggest that OPC and seston provide reliable estimates of lacustrine zooplankton biomass as long as region-specific ellipse models and size fractions, respectively, are used.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".