Computer Aided Evaluation and Assessment of Aggressiveness or Tendency of Water to Form Alkaline and Sulfate Scales
Bibliographic record
Abstract
Our study introduces an interactive software tool developed in Visual Basic for predicting and evaluating the formation of hard scales in water during various operational conditions.Water properties such as pH, alkalinity, CO2 gas pressure, ionic strength, and operating temperature and pressure are required inputs for the software.It provides efficient data storage, water analysis capabilities, and output formatting.Users can input water analysis results and operating conditions using different units, generating multiple interpretable outcomes, including Langelier Saturation Index (LSI), Ryznar Saturation Index (RSI), Calcium Carbonate Precipitation Potential (CCPP), Stiff and Davis Stability Index (SDI), and Oddo and Tomson Index (OTI).Furthermore, the software predicts the dissolution rates of concrete in aggressive water for prestressed concrete cylinder pipes (PCCP).Real-time data from various sources, including the Great Man River Project (GMRP), Arabian Gulf Oil Company, Melita Oil and Gas Company, and Zueitina Oil Company, validate the software's accuracy and reliability.This software enhances water property management, improves operational efficiency, and lowers maintenance costs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".