Conceptual Framework for Assessing Sustainability of Swamp Buffalo Production Systems
Bibliographic record
Abstract
Swamp Buffalo farming plays an important role in farmers' livelihood and in satisfying red meat demand in South Kalimantan, Indonesia. The extensive (wetland) swamp buffalo production system (SPS) and the extensive and semi-intensive dryland system (DPS) are two production systems. The production systems have high complexity and require the integrated sustainability assessment approach to measure the contribution level of sustainability indicators. This study aimed to demonstrate the conceptual framework for analyzing the sustainability of buffalo production systems in South Kalimantan. The buffalo production systems in South Kalimantan were analyzed using the comprehensive assessment framework from September-December 2021. A literature review and discussion with experts, followed by a focus group discussion to perform a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was conducted. The complex problem identifies and defines the relevant Economic, Ecological, and Societal (EES) issues, and inclusive identification and analysis of relevant stakeholders were described. Issues identified during the process were translated into relevant indicators in the EES sustainability dimensions then indicators possible for EES issues were proposed. Situation analysis in this study described and identified swamp buffalo in South Kalimantan, which is currently experiencing a population decline. The gross margin and growth and reproduction performances of the buffaloes were selected for economic benefit in both systems. Total land use and soil fertility were the possible indicators in the dimension of environment relevant for DPS, while swamp sedimentation and water pollution were considered important environmental indicators in SPS. Feed availability was measured in both systems. Social dimension indicators in both systems were focused on keeping management, the function of buffalo for livelihood, time allocation to keep buffalo, characteristics of livelihood, and possible land use conflicts.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".