Bibliographic record
Abstract
CONTEXT: Noise of machines in various agro-based industries was found to be the major occupational hazard for the workers of industries. The predominant noise sources need to be identified and the causes of high noise need to be studied to undertake the appropriate measures to reduce the noise level in one of the major agro-based industries, oil mills. AIMS: To identify the predominant noise sources in the workrooms of oil mills. To study the causes of noise in oil mills. To measure the extent of noise exposure of oil mill workers. To examine the response of workers towards noise, so that appropriate measures can be undertaken to minimize the noise exposure. SETTINGS AND DESIGN: A noise survey was conducted in the three renowned oil mills of north-eastern region of India. MATERIALS AND METHODS: Information like output capacity, size of power source, maintenance condition of the machines and workroom configurations of the oil mills was collected by personal observations and enquiry with the owner of the mill. Using a Sound Level Meter (SLM) (Model-824, Larson and Davis, USA), equivalent SPL was measured at operator's ear level in the working zone of the workers near each machine of the mills. In order to study the variation of SPL in the workrooms of the oil mill throughout its operation, equivalent SPL was measured at two appropriate locations of working zone of the workers in each mill. For conducting the noise survey, the guidelines of Canadian Centre for Occupational Health and Safety (CCOHS) were followed. Grid points were marked on the floor of the workroom of the oil mill at a spacing of 1 m x 1 m. SPL at grid points were measured at about 1.5 m above the floor. The direction of the SLM was towards the nearby noisy source. To increase accuracy, two replications were taken at each grid point. All the data were recorded for 30 sec. At the end of the experiment, data were downloaded to a personal computer. With the help of utility software of Larson and Davis, USA, equivalent SPL and noise spectrum at each reading was obtained. Noise survey map of equivalent SPL was drawn for each oil mill by drawing contour lines on the sketch of the oil mill between the points of equal SPL. The floor area in the oil mill where SPL exceeded 85 dBA was identified from the noise survey map of each oil mill to determine the causes of high level of noise. Subjective assessment was done during the rest period of workers and it was assessed with personal interview with each worker separately. Demographic information, nature of work, working hours, rest period, experience of working in the mill, degree of noise annoyance, activity interference, and psychological and physiological effects of machine noise on the worker were asked during the interview. These details were noted in a structured form. STATISTICAL ANALYSIS USED: Nil. RESULTS: The noise survey conducted in three renowned oil mills of north-eastern region of India revealed that about 26% of the total workers were exposed to noise level of more than 85 dBA. Further, 10% to 30% floor areas of workrooms, where oil expellers are provided have the SPL of more than 85 dBA. The noise in the oil mills was dominated by low frequency noise. The predominant noise sources in the oil mills were seed cleaner and power transmission system to oil expellers. Poor maintenance of machines and use of bamboo stick to prevent the fall of belt from misaligned pulleys were the main reason of high noise. Noise emitted by the electric motor, table ghani and oil expellers in all the oil mills was well within 85 dBA. Subjective response indicated that about 63% of the total workers felt that noise interfered with their conversation. About 16% each were of the opinion that noise interfered in their work and harmed their hearing. About 5% of workers stated that the workroom noise gave them headaches. CONCLUSIONS: The workers engaged in the workrooms of the oil mills are exposed to high noise, which will have detrimental effect on their health. The poor maintenance of drive system was found to be the main reason for high noise level.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".