Modeling taper and volume of Sal (Shorea robusta Gaertn. f.) trees in the western Terai region of Nepal
Bibliographic record
Abstract
Volume and taper equations are used for estimating timber volume and biomass of a tree. Despite their usefulness, precise and site specific equations are still lacking for commercially important tree species in Nepal. The study was carried out at Chandak Chatiya Mahila Community Forest in Bardia district and Lumbini Collaborative Forest of Saljhandi in Rupandehi district in western Terai of Nepal. A destructive sampling method was used and selected fifteen Sal trees (Shorea robusta Gaertn. f.) from Saljhandi (site 1) and eighteen trees from Bagnaha (site 2) randomly to calibrate an individual tree volume and a stem taper function. At first, a non-linear stem taper function was calibrated using stem diameters outside bark at different heights above ground as response variable and D (diameter at breast height), H (total height), h (height of interest) as predictors. Then, effect of crown characteristics on stem taper was evaluated. As stem HCB (height to crown base) was found to affect stem taper, its usefulness in existing stem volume equation was tested. Empirical relationships between V (stem volume) as a response variable and D, H, HCB and sites in Bardia and Rupandehi districts as predictors were established using a linear mixed modeling approach. Our result showed that, instead of H, use of HCB in stem volume equation increased model prediction accuracy and reduced prediction bias. Applicability of the suggested models for predicting individual S. robusta tree volume and stem taper is discussed. Banko JanakariA Journal of Forestry Information for Nepal Special Issue No. 4, 2018, Page: 76-83
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".