Environmental health impacts of tobacco farming: a review of the literature: Table 1
Bibliographic record
Abstract
OBJECTIVE: To review the literature on environmental health impacts of tobacco farming and to summarise the findings and research gaps in this field. METHODS: A standard literature search was performed using multiple electronic databases for identification of peer-reviewed articles. The internet and organisational databases were also used to find other types of documents (eg, books and reports). The reference lists of identified relevant documents were reviewed to find additional sources. RESULTS: The selected studies documented many negative environmental impacts of tobacco production at the local level, often linking them with associated social and health problems. The common agricultural practices related to tobacco farming, especially in low-income and middle-income countries, lead to deforestation and soil degradation. Agrochemical pollution and deforestation in turn lead to ecological disruptions that cause a loss of ecosystem services, including land resources, biodiversity and food sources, which negatively impact human health. Multinational tobacco companies' policies and practices contribute to environmental problems related to tobacco leaf production. CONCLUSIONS: Development and implementation of interventions against the negative environmental impacts of tobacco production worldwide are necessary to protect the health of farmers, particularly in low-income and middle-income countries. Transitioning these farmers out of tobacco production is ultimately the resolution to this environmental health problem. In order to inform policy, however, further research is needed to better quantify the health impacts of tobacco farming and evaluate the potential alternative livelihoods that may be possible for tobacco farmers globally.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".