Identification of Price Leading Indicators for Construction Resources
Bibliographic record
Abstract
Resources prices fluctuation in many countries is an influential factor in construction projects' characterization of schedule slippages and cost overrun.Each country's market may be defined by its influential materials.In Egypt, Cement, and steel bars have major contribution to most of the construction activities.Changes in the material prices, especially drastic ones, are major threats to any contractor's plans as well as owners' budgets.Hence, timely forecasting of these changes can be a major advantage to contractors or owners.Prior to forecasting the fluctuations, identification of the leading indicators and investigation of the best time lag between these indicators and the predicted prices shall be conducted.Many researchers utilized statistical tests to identify leading indicators of cost indices, however, each resource might have its own leading indicator and unique lag time.This research aims at identifying the leading indicators of Egypt's main material prices through utilizing statistical tests such as Granger causality test.Egypt's macroeconomic indicators GDP, money supply, external debt, lending rate, stock market index, and U.S. dollar to Egyptian pound exchange rate were found to be the leading indicators of steel price.Lending rate, unemployment rate, and foreign reserves were found to be cement prices leading indicators.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".