Identification of Price Leading Indicators for Construction Resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it