Are seasonal lakes as productive as permanent lakes? A case study from Ireland
Bibliographic record
Abstract
This paper compares, for the first time, nutrient levels and chlorophyll a measured in a set of seasonal lakes with those reported for permanent lakes in the literature. Twenty-two turloughs (karstic seasonal lakes) in western Ireland were sampled monthly from the onset of flooding (October) until they dried out (6 to 9 months). The turloughs showed similar levels of nutrients and chlorophyll a to those reported for Irish and international lakes. Chlorophyll a peaked between November and February in the majority of turloughs, sometimes with values higher than those measured in mesotrophic lakes in summer. A significant log-linear regression was found between total phosphorus and chlorophyll a, which suggests P limitation of algal biomass in the majority of the turloughs. The regression characteristics were not significantly different than those described in similar studies of permanent lakes. Patterns in seasonal variation of nutrients are also presented, their underlying causes being discussed in relation to their transport within catchments. Our results show that despite being predominantly winter phenomena, turloughs can be as productive as permanent 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".