Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The aim of the paper is to present the impact of human errors in maintenance as found in the literature in order for practitioners to be aware of their impact and develop actions to mitigate their effect. Design/methodology/approach The paper systematically categorizes the published literature and then analyzes and reviews it methodically. Findings Human error in maintenance is a pressing problem . Practical implications A maintenance person plays an important role in the reliability of equipment. It is also a well‐known fact that a significantly large proportion of total human errors occur during the maintenance phase. Human error in maintenance is a subject which in the past has not been given the amount of attention that it deserves. This paper will be useful to people working in the area of maintenance engineering, as it presents a general review of literature published on maintenance errors in various sectors of industry. Originality/value The paper contains a comprehensive listing of publications on the field in question and their classification according to industry. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of human error in maintenance.
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.
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 | 0.019 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| 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 it