Impact of Agroforestry Practices on Vegetation Diversity and Structure in Pesawaran, Indonesia
Bibliographic record
Abstract
Agroforestry is the practice of combining agricultural and forest crops.In Indonesia's forest areas, agroforestry systems are widely used.Agroforestry in Indonesia makes many contributions to food and the environment.Agroforestry is often carried out in forest areas, to get around this usually uses HKm (social forestry).Among them are those who carry out the Register 20 Pesawaran Regencies in Lampung Province, permit to use HKm, but the composition of many plant types almost resembles forests, so research has not been carried out to analyze various types of plants and determine plant stratum in agroforestry.This research aims to identify types and stratum on agroforestry land.The type of data collected includes primary data, namely plant vegetation analysis (IVI, SDR and H) and determination of plant stratum.Based on the IVI plant vegetation analysis, the tallest plant at the tree stage is the durian, at the pole level is the cocoa, at the sapling level is the cardomon, and at the seedling level is the bayur and jengkol.Of all plants, cocoa plants have the largest SDR.The species diversity index (H) is moderate.The plant stratum is divided into four stratums (B, C, D, E).
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".