MétaCan
Menu
Back to cohort
Record W2087944907 · doi:10.5539/sar.v2n1p44

Lumbering Activities and Market Equilibrium Analysis of Timber Industry in South Western Nigeria

2012· article· en· W2087944907 on OpenAlexvenueno aff
Kassim Adekunle Akanni

Bibliographic record

VenueSustainable Agriculture Research · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)Wood industryProfit (economics)TruckAgricultural economicsMarket shareAgricultural sciencePrice elasticity of demandSupply and demandEconomicsBusinessEconomyNatural resource economicsEnvironmental scienceForestryEngineeringFinanceMicroeconomicsGeography

Abstract

fetched live from OpenAlex

<p>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, d<sup>2 </sup>(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.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.014
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.406
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueSustainable Agriculture ResearchSame topicEfficiency Analysis Using DEAFrench-language works237,207