Aquatic macrophyte richness in Danish lakes in relation to alkalinity, transparency, and lake area
Bibliographic record
Abstract
We examined the relationship between environmental factors and the richness of submerged macrophytes species in 73 Danish lakes, which are mainly small, shallow, and have mesotrophic to hypertrophic conditions. We found that mean species richness per lake was only 4.5 in acid lakes of low alkalinity but 12.3 in lakes of high alkalinity due to a greater occurrence of the species-rich group of elodeids. Mean species richness per lake also increased significantly with increasing Secchi depth. No significant relationship between species richness and lake surface area was observed among the entire group of lakes or a subset of eutrophic lakes, as the growth of submerged macrophytes in large lakes may be restricted by wave action in shallow water and light restriction in deep water. In contrast, macrophyte species richness increased with lake surface area in transparent lakes, presumably due to expansion of the area colonised by submerged macrophytes. Thus, the size of the colonised area is a better predictor of species richness than lake surface area. The strong increase in species richness accompanying greater transparency can be accounted for by the combined effect of higher colonised area and higher habitat richness along gradients of deeper macrophyte growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".