Emergency and Growth of Andiroba Seedlings (Carapa guianensis Aubl.) in Function of the Seeds
Bibliographic record
Abstract
Andiroba (Carapa guianensis Aubl.-Meliaceae) is considered an Amazonian forest species with great potential for multiple use in natural forests. Because it is a native species of the Amazon rainforest, it has great socioeconomic importance for the extractive population due to the oil extracted from its seeds. The speed of emergence is an important factor in the establishment of seedlings, since the longer the seeds remain inside the soil, the seeds are in the soil, the greater the chances that the seeds will be attacked by fungi and soil insects. The objective of this work was to determinate the speed of andiroba emergence (Carapa guianensis Aubl.) seedlings and the development. The experiment was carried out in a greenhouse belonging to Federal Rural University of Amazonia (UFRA)-Capitão Poço, Brazil. Fruits of C. guianensis were collected in floodplain areas in northeastern of Pará. The treatments were arranged according to the seed mass. Were calculated 5 variables after sowing (emergence speed index; height; interference of the mass of andiroba seeds at the height of the seedlings stem; interference of the mass of andiroba seeds in the number of leaves; interference of the mass of andiroba seeds in the number of leaflets). The experimental design was completely randomized, consisting of 4 treatments with 5 replicates with 5 seeds per replicate (5 seeds per vessel), totaling 25 seeds per treatment. The data were statistically analyzed using the Tukey test at 5% probability with the software Assistat 7.6. The seed mass classes did not influence ESI (Emergence Speed Index). The seed mass promoted significant effects on 4 variables. The mass of the seeds of andiroba does not influence the ESI of seedlings for it own production. The heavy and very heavy seeds generated more developed plants.
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 | 0.000 |
| 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 | 0.000 | 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".