The Economic Impact of Climate Change on the Agricultural System in Fiji
Bibliographic record
Abstract
Climate change is one of the most crucial challenges identified in this century for the Pacific Region, such as Fiji, Samoa, Solomon Islands and many more. Citizens of Fiji have gone through peculiarly climatic and weather conditions over the past years like globalization, which had led to many consequences, especially in the agricultural sector which is the main income of many livelihoods not only in Fiji but in other Pacific countries as well. Climatic conditions have been changing adversely from past decades, such as temperature, rise in the sea level, precipitation changes, atmospheric composition changes, flooding, and tropical cyclones. These changes have led to alterations in the environment, thus, affecting crop and livestock production in the agricultural system. For instance, crops that require specific soil and temperature situations are vastly influenced when the temperature level changes suddenly, making the crops vulnerable to adapt to the alterations and therefore, the crops eventually die. Likewise, animal species also get affected by temperature changes, such as heat stress which specifically affects the fertility of male and female livestock. Due to these events, Fiji’s economies have also been affected since agriculture plays a vital role in boosting our economy through local market sales and exporting. Thereby, this review illustrates the impacts of climate change and ways to move forward/ solutions, for example, FAO (Food and Agriculture Organization) and Pacific Islands Climate Change Assistance Program (PICCAP) have supported Fiji in bringing adaptation programs for preparing farmers and all other individuals on the upcoming climatic conditions such as adapting tolerant crops that can handle droughts and other adverse weather conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".