Working in partnership to communicate down-to-earth messages on integrated soil fertility management.
Bibliographic record
Abstract
The Africa Soil Health Consortium (ASHC) works to build capacity and develop exemplar development communication materials, primarily for smallholder farmers in sub-Saharan Africa on integrated soil fertility management (ISFM).Soils in most sub-Saharan countries have inherent low fertility and do not receive adequate nutrient replenishment.Consequently, yields are relatively low, despite high potential for improvement 1 (FAO 2001).Improving soil health is the underlying challenge that ASHC seeks to address.ASHC's approach is to work with partners to achieve both access to and understanding of ISFM knowledge.Accessing reliable ISFM information, as well as addressing the research/end-user divide, has been documented by a number of studies (Adolwa et al., 2012; Damisa & lgonoh, 2007 2 ; Odendo et al. 2006 3 ; Sanginga and Woomer, 2009 4 ). 1 ftp://ftp.fao.org/agl/agll/docs/foodsec.pdf 2 Damisa MA and Igonoh E (2007) An Evaluation of the Adoption of Integrated Soil Fertility Management Practices Among Women Farmers in Danja, Nigeria.The Journal of Agricultural Education and Extension, 13(2), pp.107-116 3 Odendo M, Ojiem J, Bationo A and Mudeheri M (2006) On-farm Evaluation and Scaling-up of Soil Fertility Management Technologies in Western Kenya.Nutrient Cycling in Agroecosystems, 76, pp.369-381 4 Sanginga N and Woomer PL (2009) Integrated Soil Fertility Management in Africa: Principles, Practices and Developmental Process.Nairobi: Tropical Soil Biology and Fertility Institute of the International Centre for Tropical Agriculture. How to cite this paper
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.022 |
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".