Antiulcerogenic, Anti-Secretory and Cytoprotective Effects of Piper Cubeba (L.) on Experimental Ulcer Models in Rat
Bibliographic record
Abstract
Background. The municipality of El Zulia, Norte de Santander, has historically been an agricultural region, which has had to overcome and confront important challenges in its economic development derived from the armed conflict, the lack of state investment and the effects of climate change. Objective. This study aims to identify the main vulnerability factors that affect coffee growers and analyze the resilience strategies they have implemented to adapt to economic and climatic adversities. Methodology. The study is carried out using a mixed approach through 10 semi-structured interviews, directed to local coffee growers to understand their experiences with coffee cultivation and production practices. In addition, a survey is applied to 304 producers that allows obtaining qualitative and quantitative elements such as gender, educational level, source of resources to buy the farms, access to basic services, farm size, monthly income and monthly expenses. Results. The analysis of the information revealed that the main vulnerability factors for coffee growers include climate change, labor shortages in the region, the incidence of pests and diseases, and the lack of adequate road infrastructure. Despite these challenges, coffee growers have demonstrated a remarkable capacity for resilience, backed by their family roots and a deep tradition in coffee growing. Conclusions. It is concluded that, in light of the identified vulnerability factors, coffee growers highlight the support of the National Federation of Coffee Growers as their main strength, promoting improvements in agricultural practices, crop diversification, and the adoption of innovative techniques. However, they emphasize the need for greater support from the National Government through public policies that reinforce these initiatives, facilitate access to credit, subsidize fertilizers, and promote investment in infrastructure to improve their productive capacity.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".