Biological control and the Nagoya Protocol on access and benefit sharing – a case of effective due diligence
Bibliographic record
Abstract
Biological control agents must be collected and utilised in compliance with the Nagoya Protocol on Access and Benefit Sharing (ABS) which is being implemented independently by each country that is signatory to the Protocol. By March 2018, 50 countries had legislation in place with an additional 54 designing their Legislative, Administrative or Policy Measures having become Party to the Protocol. Apart from the problem of dealing with the many different mechanisms countries are putting in place, it is often difficult to find relevant information on the ABS Clearing House and to access and receive appropriate responses from the National Focal Points or Competent National Authorities. We feel that a lot of time is lost on both sides (National authorities and scientists seeking information), and the process would benefit from streamlining. Also, open questions remain, such as how to deal with the generation digital sequence information and what specific activities are considered utilisation, especially for biological control. CABI has pro-actively developed an ABS policy and best practices for its staff to try and comply with the Nagoya Protocol. In addition, CABI has started negotiations with several provider countries, beginning with its member countries, to have its ABS policy and best practices recognised, considering the non-monetary benefits typically associated with biological control. The Nagoya Protocol was born out of the necessity to guarantee the fair and equitable sharing of benefits arising from the utilisation of genetic resources. However, it should not hinder the development of best practice solutions to protect exactly these genetic resources from threats like invasive species. It is important that research and development that addresses global societal challenges are not impeded and that science and its output are recognised as a way to preserve and use genetic resources in an equitable way.
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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 | 0.122 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.043 | 0.026 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".