Intersection of corrosion prevention strategy and practice
Bibliographic record
Abstract
Purpose The purpose of this paper is to objectively evaluate the cost benefit of applying corrosion prevention coatings throughout a mid-life logistics fleet supporting the Canadian Army. Design/methodology/approach A database of maintenance records for an Army logistics vehicle throughout a four-year study period is mined. Statistical analysis (primarily ANOVA) accounting for the frequency of treatment and geographic region is executed. Findings Statistical analysis indicates counter-intuitive results. Vehicles that are most frequently treated to prevent corrosion incur the highest maintenance costs. Consultation with operational units suggests that a strategic approach to corrosion prevention is largely absent. Instead, vehicles are treated on an ad hoc basis, or – equivalently – on an as available basis. Practical implications Among high tempo organizations, vehicles most readily available to maintenance support are those that are in the greatest state of disrepair. Vehicles that are in better condition are preferred by operators for daily operations and are not available. Consequently, the vehicles that are subject to preventative maintenance most often are those near their end-of-life or are in disrepair and therefore gain little through further investments in corrosion prevention initiatives. Originality/value Clearly, having corrosion prevention compounds applied to a fleet on an ad hoc basis suffers from the natural bias occurring among operators to retain vehicles in best condition for operational purposes. Corrosion prevention requires a more strategic approach including disciplined maintenance operations in order to provide dividends on a fleet-wide basis.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".