From Months to Days—An Efficient Microsoft-Excel Database: A Case of Dam Maintenance in United Arab Emirates
Bibliographic record
Abstract
The subject of this study is the phenomenon of the success of an electronic program (Microsoft-Excel®) for the dam maintenance database. Project improvement includes three phases with a duration of 2 years. The current Excel programming is free, in contrast to specialized software, which costs hundreds of thousands of dollars. More than 6,000 cells were programmed with many smart logarithmic equations. The purpose of this study is to explain, in a practical and theoretical framework, the reasons that cause the distinguished project results (reduction of the completion of maintenance reports from months to 5–10 working days) with low-cost efficiency. Therefore, this leads to increasing the agility of maintenance work fulfilments (effectiveness). Particularly, 11 criteria were designed to compare the status of the project; before and after; improvement. Theoretically, this study adopted the case-study research approach. It aims at setting enablers (actions) that lead to a specific outcome (theory - model building) of the current practice. The study sample was represented by the project team themselves. As a result, the study model (theory) estimated six elements (variables). Generally, the reasons for the success included three elements: technical, managerial and personal factor. These factors led the maintenance project to achieve high efficiency, accuracy and effectiveness. As a comprehensive recommendation, if any global construction institution (such as the Dams Department) intends to implement a similar e-project, its focus should not be limited to the electronic aspect; rather, the managerial and personal aspect should be the focus. In addition, this e-project is a ready base for an optimized technological platform.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".