Assessment of a pragmatic strategy to improve health of kacang goats in subsistence agricultural communities in Indonesian Borneo
Bibliographic record
Abstract
Poverty limits options available to smallholder, subsistence farmers to prevent or reverse livestock malnutrition and endoparasitism, two of the global drivers of goat morbidity and mortality in resource-constrained, tropical, agricultural communities. Our first study objectives describe changes observed in body condition and anaemia after implementation of three feasible and simple husbandry changes to improve health of smallholder herds of kacang goats in rural, Indonesian Borneo. These changes included routine hoof trimming and increased access to food and fresh water. We observed an impressive six-fold decrease in emaciated animals from 26% to 4% and an almost doubling of goats in ideal body condition from 29% to 54% after fourteen months of improved hoof care and nutrition. The second study objective described herd health changes observed fourteen months after adding a targeted, selective, refugia deworming regimen to the enhanced husbandry program. We observed a significant decrease in proportion of anaemic goats from 88% to 74% fourteen months after initiating the targeted selective herd anthelmintic treatment. Impoverished, smallholder subsistence agricultural communities with limited resources should first initiate feasible husbandry enhancements to begin improving overall herd health especially when anthelmintic expense or availability delays establishing an ideal program which includes a deworming component.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".