Rapport sur le projet "NOMAD". Enquête sur l'application des exigences essentielles de la Directive Machines relatives à l'information sur le risque bruit fournie dans les notices d'instructions par les fabricants.
Bibliographic record
Abstract
The NOMAD project was a survey to examine the noise-related content of instructions supplied with machinery offered for purchase in the European Economic Area (EEA). The project collected more than 1,500 sets of instructions from machines covering 40 broad machine-families from 800 different manufacturing companies. The information in these instructions was analysed to determine compliance with the requirements of the Machinery Directive, and assess the quality of information.The general state of compliance of machinery instructions with the noise-related requirements of the Machinery Directive was found to be very poor: 80% of instructions did not meet legal requirements. The main causes of failure to meet legal requirements were: some or all required numerical values relating to noise emissions were missing; and, where values were given they were not traceable to machine operating conditions or measurement methods, and not credible either against stated conditions/methods or as warnings of likely risk in real use.As a consequence, it is considered highly likely that, in making a machinery procurement decision, employers are prevented from taking noise emissions into account, and are prevented from understanding what is necessary to manage the risks from noise relating to equipment that is procured.Recommendations are made for actions aimed at bringing about a global improvement to the current situation. The recommendations consist of targeted actions that are achievable on a large scale, can be carried out within existing frameworks and are expected to have measureable outcomes. Targeted actions are proposed aimed at raising awareness of the legal requirements, responsibilities and actions required among the various groups who have parts to play in the system - machine manufacturers, standards-makers, machine users, and occupational safety and health professionals. Proposals are also made towards enforcement campaigns aimed at machine manufacturers, and towards targeted market surveillance activities.Recommendations are also made aimed at providing, or improving, tools and resources for both machinery manufacturers and market surveillance personnel.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".