SUDBIOTECH: A Training Initiative in Plant Biotechnology Dedicated to Scientific Communities in Developing and Emerging Countries
Bibliographic record
Abstract
The SudBiotech project is targeting PhD and Masters Programmes in Universities and Excellence Research Centres located in developing countries. It proposes an integrative and multidisciplinary approach under the form of a one-week discovery and training itinerary. SudBiotech is aimed at addressing various different fields of Plant Biotechnology, under the specific socioeconomic context of scientific communities from these countries. Our main goal is to train students, research staff, deciders, professionals and journalists to the basic knowledge underlying applications of plant biotechnologies, in order to both update the amount of knowledge which is requested at Master’s level and to acquire a solid body of information which is indispensable for any decision making, in a field of research which is often the target of political, social and media-related pressure. The team of Professors involved in the project shares a strong experience in teaching and training in overseas French Speaking Universities (AUPELF-UREF projects in Marocco, Tunisia, Togo, Côte d’Ivoire, TEMPUS Programme in Lebanon, AUF-Actions de Recherche en Réseau in Benin). The team is composed of senior scientists acting in various complementary fields, namely: Biochemistry/Physiology (A. Nato), Plant Breeding/Molecular Biology (Y. Henry), Tropical Agriculture/Epigenetics (A. Rival). Thanks to these complementarities, SudBiotech is able to propose a training itinerary which is diversified and integrated, covering areas from the plant cell and its original potentialities to the most recent industrial applications of plant biotechnologies (GMOs), their public acceptance in developing and emerging nations and their applicability to tropical plant commodities. The training offer is amplified and enriched through the active role of local research and training staff, who are encouraged to actively participate in the SudBiotech project under various forms including lectures, field visits, practical sessions, etc. SudBiotech relies on original research results, which support and illustrate the various different basic notions evoked during lectures. The case of the bio-production of high added value pharmaceutical products by genetically engineered cells or plants under confined condition is a good example of this integration. Our priority is to establish a long-term, continuous system for training and capacity building, based on appropriate tools for scientific communities in developing and emerging countries: training of PhD students, job opportunities in their native country/region, overseas training, access to scientific information and literature and access to funders and international networks. It is important to note that any training material which is produced under the framework of SudBiotech is graciously given without any Intellectual Property Rights to partner institutions, in order to constitute a local basis for training in Plant Biotechnologies in beneficiary countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".