Costa Rican Small Business Moving Towards Sustainable Future: A Case of Varcli Pinares
Bibliographic record
Abstract
Abstract Among the major industries in Costa Rica, agriculture has contributed significantly to the country's economy. Felipe Vargas established ‘Varcli Pinares’ 11 years ago to produce good quality bananas through sustainable operations of natural systems. This case study explores the complex dynamics surrounding the Varcli Pinares agricultural farming, aiming to shed light on its multifaceted nature and provide insights into its outcomes. The company invests in circular economy by executing decarbonisation through capture of CO2 wastes and protection of forests. The business pioneered in reducing water consumption and implementing solar energy usage in banana production through innovative tools and technologies while engaging in no-herbicide practices. The unique, sustainable packaging created through waste makes a visual impact and guides customers to online platforms. The company adheres to sustainable practices while impacting the community positively. However, as a small business maintaining sustainable practices throughout the entire supply chain and competing with big companies is difficult for Varcli Pinares. Nevertheless, the company believes its simplicity and adaptability will help it thrive in the competitive industry. Through a rigorous and systematic approach, the case study aims to contribute to the existing body of knowledge in sustainable agricultural farming and provide valuable insights for practitioners and stakeholders alike.
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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 | 0.001 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".