Sensitivity of zooplankton for regional lake monitoring
Bibliographic record
Abstract
We present a general method for evaluating and selecting indicators for regional monitoring based on an analysis of the relative magnitude of spatial and temporal components of variation. As part of a pilot survey of 355 lakes in the northeastern U.S.A., we sampled zooplankton assemblages and evaluated candidate indicators for their components of variance. Indicators with high sensitivity for status estimation show strong lake-to-lake differences as defined by the ratio of the spatial component of variance divided by the remaining components. Sensitivity generally increased within spatial partitions of the larger Northeast region. Calanoid abundance indicators showed the highest sensitivity but only within the Adirondack Mountains and coastal/urban zone and had low sensitivity in region-wide estimates. Rotifer, cyclopoid copepod, and cladoceran abundances showed low sensitivity irrespective of subregions. Richness indicators also showed low sensitivity across subregions. We conclude that sensitivity can be increased for many zooplankton indicators with increased revisit sampling and with refinement of spatial boundaries. Our results also show a good correspondence within abundance indicators between first and second visits within a year. Hence, the single visit protocol of the sampling design provides a reasonable snapshot of the general structure of a lake's zooplankton assemblage.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".