Lumbering Activities and Market Equilibrium Analysis of Timber Industry in South Western Nigeria
Bibliographic record
Abstract
The local lumbers, without restraints, are continually exploiting the Nigerian forests. This trend causes a massive reduction in the economic, recreational, water purification and other benefits derivable from the forest and forest products. The overall implication of this is that there is a shortfall in the market supply of wood and wood products in South Western Nigeria. This situation poses some challenges to the government, private operators and other stakeholders in timber industry. This study therefore, investigated the implications of the activities of the lumbers and the market equilibrium structure of wood industry in the region. Both the stochastic production frontier and market equilibrium analyses were done. Fuel, credit and trucks’ carrying capacities significantly determined the quantity of output of the lumbers. The estimates of the sigma square, d2 (0.3811) and gamma, g, (0.9001) were positively significant at 1% level. The log likelihood function was large (24.6672) indicating a good fit. Estimated demand elasticity indicated that N1.00 increase in market price produced 14.14 %, 52.73% and 55.12% decrease in the quantities demanded for 2x3x12, 3x4x12 and 2x6x12 wood sizes. To further increase the output level of wood products in the region therefore, it is necessary to stabilize fuel prices and make the product available to the lumbers at the required time and quantities.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".