The effect of sampling scales on the interpretation of environmental drivers of the cyanotoxin microcystin
Bibliographic record
Abstract
Microcystins (MC), a class of cyanobacterial toxins, were measured in one shallow moderately eutrophic lake over 3 years and compared with results from multi-lake regional studies taken from the literature to understand how the choice of sampling regime affects observed correlations between microcystins and environmental conditions. Our results show that correlations between MC seston content and environmental factors are quite dependent upon the temporal sampling scale. The within-year variation in total MC seston content was high, almost an order of magnitude higher than that of the abiotic variables measured and that of algal biomass (Chl-a) but similar to the variation in the density of cladoceran zooplankton. At the temporal scale of seasons, no significant correlations could be detected between MC seston content and environmental variables, but at the monthly scale, pH and temperature were positively correlated with MC seston content. At the weekly scale, reactive phosphate was weakly correlated with MC seston content (with a lag of 12 d) as detected through time series analysis. Furthermore, the factors previously associated with microcystins at a larger spatial scale in among-lake studies (namely total phosphorus, total nitrogen, and light attenuation) did not explain the temporal MC seston content variation within one lake at any sampling scale.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".