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Record W2112114871 · doi:10.5539/enrr.v3n1p157

Effect of Chemical Presevatives on Service Life of Selected Wood Species Treated against Pests and Diseases

2013· article· en· W2112114871 on OpenAlexvenueno aff
N. H. Ukoima, Uko E. R.

Bibliographic record

VenueEnvironment and Natural Resources Research · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsPreservativeGmelinaToxicologyChemistryHorticultureBotanyBiologyFood science

Abstract

fetched live from OpenAlex

The effect of different chemical preservatives on service life of selected wood species was evaluated in a field plot at the Forestry research site, University of Uyo, Akwa Ibom, Nigeria. Three wood species, Triplochiton scleroxylon K. Schum (Obeche), Gmelina arborea Linn (Gmelina) and Terminalia ivorensis A Chew (Idigbo- Black afara) were used for this study. Two chemical preservatives namely; acid copper chromate and copper citrate at concentrations of 0%, 0.37%, 0.75% and 1.5% each and a mixture of the two preservatives (mixed preservative) at the given concentrations were used in treating stakes of these wood species and their retentions determined. Service life of the stakes was evaluated at the end of six months and insect pest and fungi that infected them identified. The result indicated that obeche had the highest mean retention of 4.04 kg/m3.The result also showed that the retention increased with increase preservative concentration, the highest retention of 6.80 kg/m3 was obtained with mixed preservative at 1.5% concentration. Treated stakes of all the three wood species lasted for the six months of the experiment while the untreated stakes failed within the period. Highest service life rating of 10 (no termite (Cryptotermes cavifrons Bank) damage) and (-) (no fungal growth) was obtained with mixed preservatives at 1.5% concentration. Based on these findings the use of these preservatives at concentrations of 0.075%- 1.5% to enhance service life is recommended.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.228
Teacher spread0.216 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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