MétaCan
Menu
Back to cohort
Record W2009252049 · doi:10.5539/eer.v4n3p50

Comparative Study of Price Variations of Basic Civil Engineering Construction Materials

2014· article· en· W2009252049 on OpenAlexvenueno aff
Felix A. Oginni, Moses A. Ogunbiyi

Bibliographic record

VenueEnergy and Environment Research · 2014
Typearticle
Languageen
FieldEngineering
TopicConstruction Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsHardwoodAgricultural economicsPeriod (music)MathematicsEconomicsEnvironmental scienceEcologyBiologyArt

Abstract

fetched live from OpenAlex

A study of varying prices of basic civil engineering construction materials was undertaken. The basic materials considered were two varieties of cement, three types of sand, and different sizes of two types of timber. Prices of these construction materials were collected from local Contractors in Oshogbo region over a period of twenty seven months. The period considered was from January 2010 to March 2012. Variation pattern in the prices was studied on monthly basis. Results indicate that prices of all the basic construction materials follow an annual cyclic pattern. Prices of the two brands of Cement were the same except at months 12, 23 and 24. The prices are stable from April to August of each year (months 4-8, and 16-20). During these periods the price of this material increased by 13.3% from ?1,500.00 in 2010 to ?1,700.00 in 2011. There were two rises per annum in the prices of sand. The first rise occurred in March and the second in the following November for sharp and fine sands. Timber prices are relatively stable within 5% variability during the months of November – March and December – March respectively for hardwood and whitewood except types H4 and W1. H4 and W1 prices were the same and they followed the same trend. The trends presented by types H4 and H5, which are same size but different species, are similar, indicating that hardwood specie is not a factor in the price variation of timbers. It can generally be concluded that the prices of the basic construction materials will stabilize during the months of April to August of every year. Although construction activities are light every January, January prices keep increasing from year to year.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.234
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEnergy and Environment ResearchSame topicConstruction Engineering and SafetyFrench-language works237,207