Combining the light-demanding <i>Araucaria angustifolia</i> with the shade-tolerant <i>Cabralea canjerana</i>: mixed plantations to produce tropical timber trees outside the Atlantic rainforest
Bibliographic record
Abstract
Many trees of high timber value require canopy cover to become established, and at present, they are only harvested from native rainforests. Other species require high radiation to establish and can be planted in monospecific stands. The main question was whether the canopy generated by a light-demanding rainforest species could protect mid-successional timber species from high radiation and extreme temperatures. We evaluated the establishment of Cabralea canjerana under the canopy of Araucaria angustifolia stands. We related growth to the number of neighbors to determine the best positions to plant C. canjerana. In one stand, we measured environmental and physiological traits, and we determined that the seedling did not suffer light or water stress. Cabralea canjerana plant establishment was successful in stands of different basal areas, and trees reached the highest growth with up to two A. angustifolia neighbors within a 5 m radius. Therefore, the number of neighbors is a tool to choose the planting location to convert even-aged to uneven-aged mixed stands. In this way, valuable native timber species that require canopy protection during the first few years can be planted outside the rainforest. This is the first report of an uneven-aged mixed plantation of two Atlantic forest timber species.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".