Assessment of Tree Species Availability Based on Sawmilling and Timber Markets Survey in Sinnar State, Sudan
Bibliographic record
Abstract
This study assesses tree species availability in Sinnar state, Sudan, to identify the types of wood used, marketed and explores the selection criteria driven by the continuing demand for timber in construction, furniture, and energy sources. The research included interviews with 87 randomly selected respondents from three timber trading and sawmill companies (Elsuki, Sinnar, and Singa). The surveys utilize descriptive analysis using SPSS and Excel. Findings revealed 28 historically available timber species, of which only eight are currently on the market. Selection criteria for trading species include viability, durability, and market demand. Approximately 47.9 % of timber comes from reserved forests, mainly for sawmill use, while 31.3 % comes from private and community-managed forests. The study highlights a significant decline in the availability of timber species, with 88 % of respondents expressing concerns about this trend due to overexploitation, revealing the urgent need for conservation efforts. This study suggests planting indigenous fast-growing trees to meet the region’s timber needs.
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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 | 0.001 | 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".