Conifer demography in forest–grassland mosaics: a landscape-scale study over a 24-year period
Bibliographic record
Abstract
Our understanding regarding the demography of adult conifers in forest–grassland mosaics is still limited. I studied the landscape-scale demography and spatial distribution of the conifer Araucaria angustifolia (Bertol.) Kuntze in a subtropical forest–grassland mosaic over a 24-year period. Araucaria angustifolia is a long-lived pioneer that is expected to thrive in grasslands and forest edges better than in forest patches. I used the position of trees in aerial photographs taken in 1984 to analyze spatial patterns and a 2008 satellite image to estimate individual survivorship. Spatial distribution of trees in the grassland was aggregated and was not related to the distribution of trees in forest patches. Survivorship was higher in forest patches than in grasslands, where it showed density dependence. In forest patches, survivorship was positively related to both patch area and distance from forest edge. Crown breakage was more common in the grassland than in forest patches. In forest patches, it was positively related to crown size, number of conspecific neighbours, and patch area. Adult Araucaria angustifolia seem to benefit from angiosperm-dominated neighbourhoods relative to isolation in grasslands. Density-dependent effects, known to be widespread among seeds and seedlings, were shown to be important to adult trees as well.
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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 | 0.001 |
| 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".