The Diversity of Rattan Types at Various Height of Growing Areas in Rompo Village Lore Lindu National Park Area, Central Sulawesi Province, Indonesia
Bibliographic record
Abstract
Non-timber forest products were potential assets to generate foreign exchange. Some types of which had good prospects and were in demand in the world of trade were rattan, gondorukem, eucalyptus and cassava. The purpose of this study was to determine the rattan types diversity in Rompo Village, Lore Lindu National Park. The study was conducted in December 2018 to March 2019. This research used the "survey" method by making a plot measuring 20 m x 20 m along to 1000 m. The distance between one track to another was to 200 m. The results showed that the type of Lambang Rattan (Calamus ornatus var celebicus Becc) had the highest density of 563.75 individuals/ha, then Pai Rattan (Calamus koordersianus Becc) 229 individuals/ha, Batang Rattan (Calamus zollingeri Becc) 183 individuals/ha, Ibo Rattan (Calamus ahlidurii) 52 individuals/ha, Rattan Tohiti Botol (Calamus sp) 46.25 individuals/ha, Pute Rattan (Calamus leiocaulis Becc ex. Heyne) 11.75 individuals/ha, Karuku Rattan (Calamus macrosphaerica Becc) 10 individuals/ha and the smallest was the type of Tohiti Wulo Rattan (Calamus sp) 9.75 individuals/ha. Rattan which had the highest Importance Value Index was the Lambang Rattan (Calamus ornatus var celebicus Becc) with an Importance Value Index value of 72.14% while the rattan that had the lowest Importance Value Index was Tohiti Wulo Rattan (Calamus sp) with an INP value of 5.02%. The level of species diversity (H ') of the rattan types found in the research location was classified as moderate with an H value of 1.75. The higher the area where the rattan is grown, the fewer types of rattan that can grow and only small rattan can grow on high ground, especially Tohiti rattan and large rattan cannot be found any more like Lambang rattan.
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 | 0.001 | 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".