Air-Borne Particle Size Distribution of Wood Dust Emitted during Small Scale Forestry Operations
Bibliographic record
Abstract
Practices such as tree felling, pruning, logging and chipping wood can produce significant amount of wood particles that can be inhaled by agricultural and forestry workers, triggering an important risk to the health of workers. Especially finest fractions of dust, less than 4 µm in diameter (the respirable fraction) may cause respiratory and dermal diseases, until to the risk of developing nose and sinus adenocancer. The aim of this work was to assess the particle size distribution of wood dust produced during chainsaw operations. In two separate trials (July and December), wood logs of three different species (Eucalyptus sp., Pinus radiata and Quercus cerris) were employed in cutting tests. Two chainsaws, one electric powered by batteries and one endothermic, were employed. To characterize the particle size distribution, samplings were carried out with a dust particle counter placed in the area surrounding the tests’ site. Results showed that the dust was characterized by a major fraction of fine particles around 0.3 µm (72% of the particles from 0.3 to 10 µm). The chainsaw with endothermic engine produced more fine dust of the electric one. Obtained amounts of inhalable wood dust were very variable in values, however attention should be paid to the exposure to wood dust considering potential risks, especially in case of long times of exposure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".