Proceedings of the International Cavies Symposium, Yaoundé, Cameroon, 6-8 July 2016
Bibliographic record
Abstract
Abundant technical knowledge is available on cavy production (mostly in South America) that needs to be targeted to African conditions and packaged in simple messages using modern tools.Moreover, some applicable technical research results already exist from SSA, especially Cameroon, which has been leading in cavy research on the continent.• Cavies offer a healthy, white meat with overall high contents in amino acids and containing 'good' fat (omega 3-poly-unsaturated fatty acids), providing a link to child development and SDG #2 on reducing malnutrition, as a valuable component in nutrition-sensitive agriculture.• There is demand for cavy meat and almost no cultural or religious barriers seem to exist.• Cavy production and marketing serves gender equality and women empowerment.Despite the absence of a dedicated cavy project, participants agreed that immediate action should be taken to advance the collaboration, such as to:• Form a cavy R&D network within and across countries to exchange information and detect opportunities, e.g.soon establish a structure for communication, using ICT tools -BecA.• Pursue awareness creation in multiple ways as opportunities arise -all participants.• Actively engage in fund raising with a variety of possible donors -all participants.• Explore opportunities for South-South cooperation, especially by tapping an emerging PROCASUR-project funded by IFAD on cavy production and marketing in North Peru with its model of 'learning territory' for technology diffusion -BecA, PROCASUR.• Continue to build capacity by emphasizing cavies as one of the African livestock species in training and research conducted by BecA fellows -BecA.• Prepare peer-reviewed publications from existing results to pursue establishing cavy culture in science -all researchers.• Explore potentially interested local projects and organizations and existing facilities to take up portions of the R&D agenda to keep going the long-term engagement -all participants.African cavy culture has been dormant for decades if not centuries, trapped in a vicious circle of neglect.Continuing this vicious circle would cause yet another missed opportunity for improving livelihoods of the poor.The International Cavies Symposium has revealed a wealth of chances for African cavy culture to evolve by piggybacking on the impressive advances achieved in South America over the past decades.It appears high time that this transcontinental connection has been established and, hopefully, will develop into a vibrant multilateral partnership.
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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 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.224 | 0.028 |
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".