The effect of fertilization at planting for Pinus elliottii and Pinus caribea var. hondurensis plantations and top dressing for Pinus elliottii plantations, in Guareí, SP, Brazil.
Bibliographic record
Abstract
Pine species play important role in Brazilian economy for solid wood and resin production. However, information about the effect of fertilization on wood and resin production is scarce. Thus, to investigate the relationship between different fertilization regimes on wood/resin productivity further, this paper analyzes the effects of two fertilizing experiments, organized in 2 parts. The first part is about the effect of 5 fertilization treatments at planting for Pinus elliottii(PEE) and Pinus caribea var. hondurensis(PCH), analyzed from two to eight years after planting considering volume, diameter at breast height, basal area, total and dominant height. The second part explored the effect of varying top dressing fertilization treatments on a 17-year-old Pinus elliottii plantation. For the first experiment, fertilization at planting contributed to substantial gains for the parameters evaluated for PCH. For example, volume was 58% higher for trees which received fertilization at planting, compared to the control group. For PEE, fertilization do contributed for gains, but they were not statistically meaningful for all the characteristics evaluated, but for basal area. In the second part, results showed that top dressing fertilization and harvest year had effect on resin production and the best treatment was the number 2. Thus, we concluded that fertilization application management must take into account the factors such as the time, the amount of application and the species being fertilized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".