Classification of South Brazilian grasslands: Implications for conservation
Bibliographic record
Abstract
Abstract Aims We offer a first classification of South Brazilian grasslands (Campos Sulinos) based on quantitative vegetation data and describing grassland types in terms of dominant and indicator species. Location South Brazilian grasslands (Paraná, Santa Catarina, Rio Grande do Sul states). Methods We described vegetation plots in 167 sampling units throughout the region using a stratified nested design, totalizing 1,502 1 m² quadrats. We classified vegetation using cluster analysis based on Bray–Curtis dissimilarities, establishing three vegetation types and ten subtypes. We conducted indicator species analysis within the resulting subtypes, and for all possible combinations of subtypes. Results In the cluster analyses, a clear separation of poorly drained grasslands from the drier sites appeared. Further, a clear distinction between grasslands in the South Brazilian highland region, situated in the Atlantic Forest biome, and the grasslands of the Pampa biome, to the south, emerged, reflecting climatic and management differences. Highland grasslands showed lower species cover dominance, while in the Pampa, Paspalum notatum clearly was the most important species and the abundance of exotic species was higher. Conclusions Our study provides the first classification of South Brazilian grasslands based on quantitative vegetation data recorded in a standardized sampling design. The data support the division of grasslands into the main phytogeographic units of the region (Brazilian biome classification). Grasslands in these two regions also differ in terms of species dominance pattern (higher dominance in Pampa grasslands, likely also due to higher grazing levels) and in terms of conservation state (low presence of exotic species in highland grasslands). Our results are important for conservation policies, which can now consider the presence of different grassland types in different region, but more data will be necessary for a more detailed classification that considers different abiotic features in more detail.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".