Inventory and Mapping of Aren (Arenga pinnata Merr.) Plus Tree in Tanggamus District, Lampung, Indonesia
Bibliographic record
Abstract
A plus tree is a tree that exhibits superior phenotypic expression compared to other trees in the vicinity and, therefore, has potential for plant breeding.The study aimed to inventory and map the aren plus in Air Abang Village, Ulu Belu Sub-district, Tanggamus Regency, Lampung.Sampling was drawn by the census method, where the entire producing and tapped aren population were taken as samples.Identification for sap productivity was conducted through interviews with the farmers to determine plus trees candidates.The direct observation was carried out to determine plus tree by measuring 11 criteria of aren plus (tree health, tree age, stem circumference, number of fronds, fronds shape, leaf colour, leaf length, number of male mayangs, number of female mayangs, sap production, and sugar content).A tree was categorized as a plus if it met all those criteria.Seven out of 60 producing aren were identified as aren plus tree.This finding indicates that the village has actual land suitability for aren cultivation.The research has identified that Air Abang has the genetic potential to be a superior aren broodstock, which is significant for planting material and plant breeding.The mapping results show that the distribution of aren plus in Air Abang is relatively random.The aren plus is predominantly found in sites with low levels of competition, where the planting distance is long enough so that the tip of the aren fronds does not intersect with the crowns of other trees.
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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".