Guide for the Optimization of Material Storage during the Structural Phase of Educational Projects through the Application of the ABC Methodology
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Bibliographic record
Abstract
This article presents a guide for optimizing material storage in educational infrastructure projects, specifically during the structural phase, using the ABC methodology.The study addresses the problem of poor management and placement of construction materials in warehouses, which negatively impacts operational efficiency and costs.A total of 172 stored materials were analysed in an October project, 86 of which were used.The materials were classified into three categories: A (11 materials), B (26 materials), and C (49 materials), representing critical values for prioritization and efficient distribution.The methodology includes an ABC analysis, a Pareto diagram, and warehouse modelling in Revit, demonstrating how redistribution improves access times, inventory control, and reduces the rate of obsolete materials.The results highlight the feasibility of implementing this guide in similar projects, promoting resource management.
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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