Do nurse plants and cattle exclusion help restore Parlatore's Podocarp forest?
Bibliographic record
Abstract
In the Neotropical cloud forests of northwest Argentina, cattle ranching has been historically the primary economic activity, with potential negative impacts on the ecosystem. Understanding factors influencing tree regeneration is crucial for reforestation efforts in grazed areas. Given the limited understanding of Parlatore's Podocarp ( Podocarpus parlatorei Pilg.) regeneration ecology, a unique gymnosperm species in the Southern Yungas' Montane Forest, we evaluated the growth and survival of its saplings for 1 year after planting them both inside and outside an exclosure, and in association with or without unpalatable nurse plants. Inside the exclosure, sapling survival was 100%, regardless of nurse plants. However, outside the exclosure saplings associated with nurse plants had 19.5 times higher survival probability. Growth rates were higher inside the exclosure and for saplings associated with nurse plants. The positive effect of nurse plants on growth was similar inside and outside the exclosure. Cattle browsing and trampling on Parlatore's Podocarp were observed for the first time, highlighting a previously undocumented threat to the species. Our findings offer valuable insights for ecological restoration, potentially suggesting strategic sapling planting near unpalatable plants and considering cattle exclusion in key areas to enhance long-term restoration success in the Southern Yungas' Montane Forest.
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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 | 0.000 | 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".