Cluster analysis of fatal accidents series in the INFOR.MO database: analysis, evidence and research perspectives
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
The state of the application of the techniques of cluster analysis does not include the work accidents. The applications more established for statistical data analysis include pattern recognition, image analy-sis and information retrieval. The aim of this study is to provide a quantitative assessment, based on techniques of statistical processing of historical data in order to highlight the causality between the accident and predictive recurring events. On the basis of information provided by the analysis, it is possible to propose preven-tive strategies targeted to reducing the number of accidents (mainly the fatal accidents). Based on the collection of fatal accidents in the Infor.MO database (INAIL), we proceeded to aggre-gate accident cases registered in order to provide cluster analysis, which with reference to generators of the danger fl ow mortal areas, could show typical accidents, namely preferential genesis that, proposing the causes of the same energy mortal fl ow, could explain a large number of events. In order to run the analysis, a methodological assumption that describes the phenomenon of acci-dents, like any algebraic entity, as the case represented in algebraic space, is requested. The n dimensions useful to describe the phenomenon are the n generators of the danger areas. Based
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it