A multi-factor evaluation of Jatropha as a feedstock for biofuels: the case of sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) is a geographical region consisting of 49 countries, out of which, 39 countries have experiences with the cultivation of Jatropha curcas L. Since the year 2000 Jatropha production escalated in the region and peaked in around 2007/2008. The major drivers of this trend were claims made about Jatropha including include its ability to grow on marginal lands, high seed and oil yields, and drought tolerant, amongst other attributes. However, the reality has shown that these attributes have not been realised. The objective of the present paper is to analyse the performance of Jatropha as a biofuel feedstock in SSA based on agronomic, economic, social and environmental factors involved in its production. Evidences in SSA show that the major challenge with Jatropha cultivation has been low seed yields, ranging between 0.1 and 2 t/ha. This in turn has led to oil yields which are not sufficiently viable for use in production of biofuels such as biodiesel. There have also been reported challenges with production on wastelands, low use of inputs, unimproved planting materials and vulnerability to pests and diseases. These have negatively affected the performance of Jatropha causing the original claims made about this energy crop not materialised in the SSA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".