Productivity of Timber Processing in Ondo State, Nigeria
Bibliographic record
Abstract
This study is aimed at evaluating the efficiency of timber processors in Ondo State, Nigeria, using Data Envelopment Analysis. Multi stage sampling technique was used to select two Local Government Areas with the highest number of sawmills, from each of which twenty saw millers were randomly selected, given a total of forty saw millers. Based on Constant Return to Scale Technical Efficiency, 35% of the saw millers were technically efficient while on the basis of Variable Return to Scale TE, 60% of the saw millers were technically efficient. About 35% of the saw millers were scale efficient. The Data Envelopment Analysis output revealed that 35% of the sampled saw millers were both technically and scale efficient and were hence operating at the most productive scale size. About 65% of the saw millers were operating at sub-optimal condition. Excesses in input utilization were observed in respect of total fixed cost, costs of electricity, servicing of mill, timber from forest reserve and operation of truck; and remuneration of labour. The inefficient firms should be encouraged to emulate the operating practices of the most productive firms so as to improve their performance.
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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 | 0.001 |
| 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) | 0.001 | 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".