Assessing Sustainability Performance at Farm Level in the Kingdom of Bahrain
Bibliographic record
Abstract
Crop production in Bahrain is facing many challenges that may undermine farm's sustainability. Farmers are an important player and assessing farm’s sustainability to identify performance gaps is essential. This study aims to measures farm sustainability in Bahrain using Response-Inducing Sustainability Evaluation (RISE) tool accompanied by field visit observations. The assessment was carried out on 29 farms and applied on 8 themes. The study contributed by configuring RISE's regional data to match Bahrain's conditions through the adjustment of some evaluation functions. The application of RISE showed that the average score of the 29 farms was (63 points) which indicates the requirement of additional improvement. Farms fared well in five themes (quality of life, water use, working conditions, soil use, and farm management). Whereas it performed low in three themes (Material Use, Biodiversity & Economic viability). Quality of life theme received the highest score (76 points), while biodiversity was the lowest (34 points). RISE application was successful; however, land tenure needs to be considered to improve local sustainability. Despite the presence of lands that are classified as agriculture but unexploited, it is suggested to encourage landowners exploiting the lands or rent them out to other farmers. In addition, the development of good agricultural practises guidelines is vital.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".