The Effects of Climate Change on Animal Production in Fiji
Bibliographic record
Abstract
Climate change is a great impact on Fiji’s ecosystem including animal (livestock and marine) and crop production from past decades and still possesses a large effect on their economy as well. These climatic events include flooding, rise in ambient temperature, rise in the sea level, droughts, tropical cyclones, and all others that bring large changes to the environmental system. These large changes adversely affect animal production and its economy in Fiji. Not only this, individuals that are linked to animal production are also affected through climatic conditions such as loss of income and livestock species that die out during cyclones and other aspects. Not only the terrestrial species but the marine organisms are also affected since climatic changes bring alterations to their feeding period and the mating time leading to a vast decrease in organisms’ health, quality, and population. Consequently, the Fijian government and other Pacific organizations have brought strategies like adaptation plans to implement in animal production sectors. These plans and methods will help farmers in stimulating their farming systems and adapting to climatic changes and hence, this will lead to increased productivity and economy. The aim and objective of this review are to define and elaborate the climatic change effects on livestock and marine production in Fiji and effective solutions adapted by Fiji and other Pacific governments to refrain from adverse climate 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".