Nutritional Efficiency of Forest Species in Natural Regeneration of Tropical Forest in Brazil
Bibliographic record
Abstract
The knowledge of the nutritional aspects of native species, mainly in natural regeneration, may be important for understanding their establishment, particularly in areas with low nutrient availability soils, such as tropical soils. This study aimed to determine the biological utilization efficiency (BUE) of the nutrients N, P, K, Ca, and Mg of forest species of natural regeneration in a Lowlands Dense Ombrophilous forest fragment in Pernambuco, Brazil. A phytosociological study of the fragment was carried out and were defined the ten species with the highest absolute density (AD). Three individuals per species were selected. The N, P, K, Ca, and Mg contents were determined in the sample leaves of the species, and the foliar biomass was determined “in loco”. Nine individuals of each species were collected according to the following diameter intervals at the base (DBs): DBs<5 cm; 5≤DBs<10 cm and 10≤DBs<15 cm. The content, stock and BUE of nutrients were calculated per species. The BUE of nutrients by species varied according to the following decreasing order: P>Mg>K>Ca>N. The highest BUE of nutrients was of the species Protium heptaphyllum. In tropical soils of low natural fertility, the use of these species can be recommended in environmental reforestation projects. The difference in the nutritional demand of the forest species can indicate the planting of those with greater capacity of absorption and BUE of nutrients, being more efficient in areas of soils with low natural fertility like in the tropical forests.
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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".