Bibliographic record
Abstract
merchandising and processing, supply chain optimisation, digital transformation, and energy.This package includes a $65m participation from the Bank's own resources, along with $10m in concessional cofinancing from the Agri-Food Catalytic Financing Mechanism (ACFM) into ETGs Sustainable Linked Loan facility for financing its core value chain assets.The Agri-Food Catalytic Financing Mechanism is an internally managed Special Fund, capitalised by Canada's Department of Foreign Affairs, Trade and Development, to build markets and mobilise finance for gender-oriented and underserved agri-SMEs in Africa.Through participation in the Sustainable Linked Loan facility, the financing will be deployed to ETG's core value chains in 14 countries, namely Benin, Côte d'Ivoire, Ghana, Senegal, Nigeria, Burkina Faso, Ethiopia, Kenya, Tanzania, Uganda, Malawi, Mozambique, Zimbabwe, and Zambia.This will support ETG's processing and packaging facilities and warehouses and provide farmers with fertilisers and other agri-inputs.The Bank's financing may be deployed to up to 28 African countries based on ETG's emerging needs.The facility establishes annual sustainability key performance indicators and targets focused on decarbonisation, reforestation, zero deforestation, farmer extension services, and gender empowerment with inherent direct financial consequences for non-compliance.ETG plans to engage 600,000 smallholder farmers by 2027, with a 25% target for women farmers.This includes training on sustainable farming and improved access to resources.The project is expected to boost exports from Bank regional member countries and enhance intra-regional trade, particularly within the Economic Community of West African States (ECO-WAS), Southern African Development Community (SADC), and East African Community (EAC) regional economic blocks.(afdb.org30/10)
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.014 |
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".