THE EVALUATION MODEL OF CONSTRUCTION COMPANIES’ PERSONNEL SAFETY AND HEALTH SYSTEM
Bibliographic record
Abstract
When the economy in the country grows up and strengthens (currently the growth rates of the economy are the highest among three Baltic States), at the same time one of the most important business areas intensively develops. Sometimes it is called as the indicator of the country's economical situation ‐ construction. The facts given by the Statistics Department suggest that BVP in the second quarter of 2003 grew 9,1%, while erection working coverage in construction ‐ about 13,8 %. And if we evaluate the economic inertia influence on the construction business and the bias of accounting (not evaluated yet) ‐ we can expect even greater growth of the real coverage [1], which (the growth) as predicted will last for several years. When analyzing accidents according to harmful factors at work, we can see that a quarter of them happens when a man falls down from altitude, the sixth ‐ when he falls down due to slip, the tenth ‐ because of the active gear, mechanism, etc. Facts are shown about the accidents at work according to the factors in 2002.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".