Frost hinders the establishment of trees in highland grasslands in the Atlantic Forest ecotone region of southern Brazil
Bibliographic record
Abstract
Abstract Questions Cold temperatures and freezing may hinder the process of forest expansion in moist climate regions, as predicted by the frost hypothesis. We examined the role of frost on the survival and regrowth of tree saplings under field conditions of open grasslands. Location Cambará do Sul, Rio Grande do Sul. Methods We established a field experiment protecting one of two groups of saplings of forest species from frost in grassland areas during the winter. We monitored 672 saplings from a total of seven species for 56 days. Saplings were brought back to the laboratory to evaluate regrowth rate and height. Survival curves of the two groups were compared using a Kaplan–Meier estimator. Correlation analysis was undertaken to estimate the relationship between survival and regrowth rate and a variance analysis was used to compare plant height between both groups. Results Species respond distinctly to frost incidence, with some not being affected by frost (two gymnosperms andMyrcianthes pungens). Most protected saplings had a higher above‐ground survival rate than unprotected ones (96.4% and 73.2%, respectively). Moreover, saplings that were damaged by frost showed differences in regrowth and height according to species identity, notwithstanding the fact that those from protected plots showed higher regrowth and height (88% and 25.65 cm compared to 68% and 17.15 cm). Conclusions Our results showed clear negative effects of frost on the survival rate of aerial parts of saplings during the winter and the regeneration of damaged plants. Species‐specific traits can offer frost resistance; however, together with other disturbances, frost may hinder the establishment of forest trees over grassland areas where frost events are common during the winter.
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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 | 0.001 |
| 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".