Survival Analysis Computers at a University’s Computer Laboratory and Implication on Maintainability
Bibliographic record
Abstract
Research has shown that, after extensive use, digital devices like computers often suffer performance declines, and some even experience sudden, complete breakdowns without warning. This phenomenon is particularly disturbing for individuals who heavily rely on these devices to carry out critical tasks. Although researchers have extensively probed the causes of computer breakdowns, detailed parameters influencing the lifespan of computers remain underexplored. This paper, therefore, aims to estimate the probability associated with the continuous functioning or failure of a computer system over a specified duration, and to examine risk factors associated with failure. Delving into the mysteries of computer longevity, data on 100 computers in a designated lab at an academic environment were examined. Data was drawn from maintenance records as well as in-depth hardware assessments. Analysis revealed that, after a 4-year period of active usage, 73 of the computers remained operational, while 27 had malfunctioned. Survival analysis methods were employed to determine the probability of computers failing at specific points in time and to identify various factors contributing to early computer failure. The findings disclosed that at the two-year mark, the probability of computers remaining operational is 80%, decreasing to 62% at the three-year juncture. The median survival time was established at 3 years and 4 months. Furthermore, an analysis of causative factors revealed that computers with faulty motherboards and power supply units associates with a lower rate of survival, while computers with issues of hard drives, operating systems, and miscellaneous components has a higher rate of survival. This study provides comprehensive data-driven evidence that offers insights on the need to implement maintenance strategies to proactively extend the lifespan of computers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".