Invertebrate communities from different wetland types of Tierra del Fuego
Bibliographic record
Abstract
Abstract. 1. Loss or deterioration of wetlands, which represent highly valuable environments, is a worldwide phenomenon. Sustainable management of wetlands, however, requires detailed understanding of the factors controlling their communities. The present study report the taxonomic composition and richness of invertebrate assemblages in different wetland types in Tierra del Fuego. 2. Aquatic invertebrates from 79 freshwater wetlands in Tierra del Fuego were inventoried in January 2001 and 2002 (austral summer). All wetlands were classified into six categories: roadside pools, floodplain pools, flooded quarries, peatland ponds, beaver ponds and large ponds. The wetland type effect on the taxonomic richness was analysed by one‐way anova . To identify wetland types with similar invertebrate communities, cluster analysis has been performed using occurrence frequency of each taxa in each wetland type and the Jaccard similarity index. 3. A total of 35 taxa were identified, including 21 microcrustaceans, 12 insects, 1 gastropod and 1 cnidarian. Copepods and cladocerans were among the most frequent taxa (occurrence frequency >40%) in most wetland types. No significant differences in taxonomic richness were found among wetlands types ( P = 0.076). The cladogram based on invertebrate taxonomic composition resulting from similarity in taxonomic composition among wetland types showed three distinct clusters; one included flooded quarries, peatland ponds, beaver ponds and floodplain pools, the second one the large ponds and the third one roadside pools. 4. Our results suggest that the wetland types studied have different conservation values, like the clusters obtained in the cladogram show. Artificial wetlands, such as the roadside pools, could play an important role in maintaining connectivity between isolated fragments of pristine, natural wetlands.
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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 | 0.000 |
| 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".