Variation in wood color among natural populations of five tree and shrub species in the Sahelian and Sudanian ecozones of Mali
Bibliographic record
Abstract
There is very little published information about variation in the wood properties of African tree species. To expand markets for wood products from these species, we must understand the factors that affect wood properties, and select the best species and sites to produce wood with the preferred properties. Wood color may be affected by edaphic and climatic conditions, tree age, wood density, and other biological and environmental factors. The major objective of this research was to determine if the mean and variability of wood color variables (L*, a*, and b*) of Balanites aegyptiaca (L.) Delile, Combretum glutinosum Perr. ex DC., Guiera senegalensis J.F. Gmel., Piliostigma reticulatum (DC.) Hochst., and Ziziphus mauritiana Lam. trees varied among regions, soil types, land-use types, and terrain types; and were linearly related with geographical coordinates and mean annual rainfall in the Sahelian and Sudanian ecozones of Mali. Correlations were also investigated among color variables and between wood density and color variables. Results indicated that there was considerable intraspecific variation in wood color variables due to all factors except land-use type; variation patterns were similar for some wood color variables and species, but there were notable differences among some species; and correlations among wood color variables and wood density differed among some species.
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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".