Which physicochemical variables should zooplankton ecologists measure when they conduct field studies?
Bibliographic record
Abstract
Over the last century, a rich literature has developed describing how the physical and chemical environment influences zooplankton communities, but there is little guidance on the suite of limnological variables that should be measured by zooplankton ecologists. We performed a literature review to assess: 1) Which variables were measured most often by zooplankton ecologists; 2) Which of these variables were consistently related to zooplankton abundance and richness; and 3) Whether key variables were overlooked by investigators. Our results show that there is a core group of nine limnological variables that are measured most frequently, including lake surface area, pH, phosphorus, nitrogen, dissolved oxygen, conductivity, chlorophyll-a, maximum depth, and temperature. These variables were among those most often associated with variation in zooplankton, but several others, including dissolved organic carbon, alkalinity, and nitrate were sampled infrequently, despite showing promise as important explanatory variables. The selection of variables in past studies did not correlate with how often those variables were significant in the literature, but instead, might have been related to their ease of measurement. Neglecting to measure important variables could have implications for fundamental and applied studies that aim to understand the factors structuring zooplankton communities and their response to environmental change.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".