Evaluation of microcrustacean (Cladocera, Chydoridae) biodiversity based on sweep net and surface sediment samples
Bibliographic record
Abstract
Biodiversity (species richness and species diversity) of chydorid Cladocera were examined by weekly sweep net sampling during the ice-free period and through surface sediment assemblages in 9 limnologically different lakes in southern Finland. Species richness in the sweep net samples was low in 2 lakes with recent ecological perturbations and the highest in one oligotrophic lake. Species diversity was the lowest in 2 eutrophicated lakes and generally higher in the oligotrophic lakes. The biodiversity values in sediment assemblages showed trends similar to those in the sweep net samples; the lowest values were observed in one of the eutrophic lakes and the highest in the oligotrophic lakes. It is likely that the observed differences were due to the impact of nutrients on the development of littoral vegetation and consequently chydorid habitats and resources. The role of nutrients as a forcing mechanism on chydorid biodiversity was indicated by high and significant negative correlations between species diversity in both total phosphorus and sweep net and sediment samples. The comparison between biodiversity in the 2 sample types showed that the biodiversity values were almost consistently higher in the surface sediment assemblages than in the sweep net samples, suggesting that surface sediment analysis provides an effective alternative method to living material examination for biodiversity evaluations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".