Potential implications of the use of digital sequence information on genetic resources for the three objectives of the Convention on Biological Diversity. A submission from CGIAR to the Secretary of the Convention on Biological Diversity (CBD)
Bibliographic record
Abstract
This report has been submitted by CGIAR in response to an invitation issued by the Secretary of the Convention on Biological Diversity (CBD) calling on “relevant organizations and stakeholders to submit views and relevant information on any potential implications of the use of digital sequence information on genetic resources for the three objectives of the Convention” as input to the "Meeting of the Ad Hoc Technical Expert Group on Digital Sequence Information on Genetic Resources", 13 - 16 February 2018, Montreal, Canada. CGIAR experience to date confirms that digital genomic sequence data can play important roles in the management and sustainable use of biological diversity and in the sharing of benefits associated with the use of that diversity. Technological capacities to generate genomic sequence data have accelerated faster than capacities to enable practical use of this information. Relatively small investments in the initial generation of genomic sequences, must then be coupled with significantly larger investments to comparatively analyse genomic sequences, to link genetic variability to useful phenotypic traits or performance, to ‘optimize’ those traits, and ultimately, to develop new crop varieties for release and use in farmers’ fields. CGIAR underscores the importance of capacity building for developing country research and development organizations to generate and use genomic sequence information as part of their own conservation and crop improvement programs, and to be able to participate on equal footing in internationally coordinated and funded research and development programs. The most important benefit to be shared from the use of genomic sequence information in agricultural research and development and plant breeding is improved food and livelihood security. Other non-monetary benefits are farmers’ improved access to technologies, enhanced institutional capacities of developing country research organizations, shared research results, and local and regional economic development.
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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 | 0.077 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".