Making It Last: an Interactive Lifecycle Calculator for Selecting Water Tank Coatings
Bibliographic record
Abstract
Abstract Selection and management of coating systems for the interior and/or exterior of a water tank is no easy feat. Owners must consider different factors including cost, lifecycle, and environmental impact when making decisions about coatings. The process of selecting a coating system and maintenance plan for a steel water tank is often based solely on personal opinions about the proposed system's value. These opinions can be limited in scope and hard to verify with data. In recent years, the industry has recognized life cycle costing (LCC) as a method of decision-making for owners and engineers to determine the most economical and sustainable solution for their asset in terms of corrosion protection. AWWA1 D102-21, Coating Steel Water-Storage Tanks, recommends the aid of an economic review using a life cycle costing analysis (LCCA) to determine the best suited course of action for coating and maintaining a steel welded water tank. A collection of multiple industry papers and resources, including the recently published paper “Separating Fact from Fiction - AWWA D102 Coating Service Life” provide unbiased historical data on which coating service life and costing can be extrapolated. Using these resources, an accurate life cycle analysis (LCA) can be completed for any water tank asset. After reading this paper, the reader will have a general understanding of where to locate accurate resources for critical inputs on water tanks, the life cycle costing and environmental analysis process, and how to use a life cycle analysis as a tool for asset management.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.012 |
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".