Quality of Untraded Rattan Stem from Central Sulawesi (Indonesia) Based on the Morphology
Bibliographic record
Abstract
Rattan is a non-timber forest product with great economic value.Therefore, this study aims to determine the quality of non-traded rattan from Central Sulawesi based on the morphological characters of the rods.It was carried out to choose alternative species of rattan suitable as raw materials to fulfill the increasing demand.A descriptive method was used with wet rattan specimens obtained from natural forests as study materials to determine the quality species and levels.Moreover, complete identification and determination of morphological characters were used to determine the specific species, while the quality was determined based on the internode length, diameter, cylindricity, color, groove depth (texture), and surface appearance of the rod.Data analysis was carried out using analysis of variance followed by a mean difference test, using the SPSS Version 21 program.The results showed that the morphological characters of the 10 rattan species were distinctively different, therefore, it was used as determinants of all variables studied.Based on the results, the non-traded rattan with a relatively similar quality as the favorite traded speciess include Calamus insignis, C. minahasae, C. koordersianus, C. leptostachyus, Daemonorops lamprolepsis, D. robusta and D. macroptera.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".