Organic dust exposure induced pulmonary damage among livestock workers.
Bibliographic record
Abstract
Livestock dust contains immunologically potent substances including allergens, endotoxins, microbial compounds, bacteria, fungi, viruses, pathogenic infectious organisms, particulate matter (PM), various poisonous gases such as ammonia, hydrogen sulphide (H2S), methyl acetate, propanoic acid, heptane etc.It stimulates the immune system through inflammatory and allergenic microbial agents (molds, bacteria, virus and allergens) and microbial-associated molecular patterns (e.g., endotoxin, glucans and peptidoglycans), to result in inflammatory reactions. Farmers are at the risk of developing airway diseases resulting from repeatedly exposures on the livestock farms. There is a paucity of data on in vivo and in vitro cellular and molecular changes following multiple exposures to these livestock contaminants and their long-term impact on the environment as well as human health. The mechanisms of lung dysfunction are still largely unknown. So, there is strong need to look at the combined effect of all the components of livestock dust as stimulatory factors for respiratory hazards. The development of preventive strategies to reduce exposure will be required- in-depth and identification of factors that affect day-to-day variability in exposure.
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 | 0.000 | 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) | 0.003 | 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".