Green leafy vegetables of rural India: ethnobotany and contribution to eye health
Bibliographic record
Abstract
Recognition of the contribution of biological diversity to human health demands more scientifically sound evidence than currently exists, while the multifactorial nature of this relationship calls for innovative research frameworks. This thesis presents a multidisciplinary case study on the contribution of elements of biological diversity, namely wild and cultivated leafy vegetables, towards age-related cataract prevention in a rural developing country context. At the center of this thesis, an ethnobotanical study identified determinants of consumption of leafy vegetables and demonstrated how perceived properties and cultivation status significantly influence consumption patterns. Plant species of interest, analysed by High Performance Liquid Chromatography, were found to exhibit high concentrations of lutein and β-carotene. Drawing on ethnobotanical and analytical data, an eye hospital-based case-control study was conducted to compare leafy vegetable consumption and diversity, along with lutein and zeaxanthin intake, in female patients identified with and without age-related cataract. Conflicting results for associations between leafy vegetable species and age-related cataract, and protective associations for elements of traditional diets, including yogurt and tea, were observed. The integration of results across isolated studies in a multidisciplinary framework further reflected the complex biological, socio-economic and environmental components of eye health and leafy vegetable diversity, and highlighted new knowledge with important application in the eye health of populations at risk.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".