Iron status is associated with worker productivity, independent of physical effort in Indian tea estate workers
Bibliographic record
Abstract
Iron deficiency is the most common nutrient deficiency in the world, affecting roughly 40% of women in nonindustrialized countries. Iron is the essential element in hemoglobin, the major carrier of blood oxygen and oxidative metabolism that supports physical and cognitive performance. The relationship between iron and physical work capacity suggests that iron deficient individuals could experience reduced work output. Participants were 138 experienced tea pluckers aged 18–55 years from the Panighatta Tea Estate in Darjeeling District of northern West Bengal, India. Hemoglobin, serum ferritin, and soluble transferrin receptor were measured from venous blood. Energy expenditure was estimated from accelerometry and heart rate, and plucking productivity was measured as amount of tea plucked during the morning work session when temperature and rainfall conditions are optimal. At a given level of energy expenditure, iron deficient, anemic, and iron deficient anemic women plucked less tea during a 3-h period. The results warrant further research as to whether interventions providing supplemental iron might improve worker productivity and work efficiency. Further study should examine evidence of economic incentives for policies and programs targeting nutritional deficiencies. Novelty Anemia predicts up to 2.02 kg (9.1%) less tea plucked per 3 h, or 4.0% lower wage per 3 h, compared with nonanemic women, controlling for physical effort. An increase of 1.0 g/L in hemoglobin concentration predicts 0.71 kg (3.3%) more tea plucked over 3 h. An increase of 1.0 g/L in hemoglobin concentration predicts a 1.6% wage increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".