Habitat Loss Challenges the Conservation of Endemic Plants in Mining-Targeted Brazilian Mountains
Bibliographic record
Abstract
Summary Ironstone outcrop habitats harbour rare and endemic rupicolous plants. In southeast Brazil, they concentrate on mountaintops in the Iron Quadrangle (IQ), an intensively exploited iron ore reserve. To evaluate the current habitat availability of 32 plants endemic tocanga(ironstone outcrops) and to support priority conservation areas and actions, we compared their functional connectivity in the IQ before (1960s) and after (2014) massive habitat loss to opencast mining. The Integral Index of Connectivity and associated metrics of habitat availability were used to evaluate present and past connectivity at a threshold distance of 500 m. The overallcangahabitat loss up to 2014 was 50%. The historical configuration of 334 patches totalling 18 654 ha was already disconnected and the proportion of patches acting as relevant stepping stones was thus very low. Furthermore, in both the historical and current settings, the largest contribution to habitat availability came from ‘intrapatch connectivity’ (i.e., patch area), especially in the east sector. All the IQcangaendemics fall into the International Union for Conservation of Nature (IUCN) Critically Endangered category and require protection. The recommended strategy for their conservation is to protect large, preferably well-preserved ironstone patches. This measure will require finding the middle ground between economic development and conservation of natural heritage.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".