Global marine biodiversity in the context of achieving the Aichi Targets: ways forward and addressing data gaps
Bibliographic record
Abstract
In 2010, the Conference of the Parties of the Convention on Biological Diversity agreed on the Strategic Plan for Biodiversity 2011-2020 in Aichi Prefecture, Japan. As this plan approaches its end, we discussed whether marine biodiversity and prediction studies were nearing the Aichi Targets during the 4th World Conference on Marine Biodiversity held in Montreal, Canada in June 2018. This article summarises the outcome of a five-day group discussion on how global marine biodiversity studies should be focused further to better understand the patterns of biodiversity. We discussed and reviewed seven fundamental biodiversity priorities related to nine Aichi Targets focusing on global biodiversity discovery and predictions to improve and enhance biodiversity data standards (quantity and quality), tools and techniques, spatial and temporal scale framing, and stewardship and dissemination. We discuss how identifying biodiversity knowledge gaps and promoting efforts have and will reduce such gaps, including via the use of new databases, tools and technology, and how these resources could be improved in the future. The group recognised significant progress toward Target 19 in relation to scientific knowledge, but negligible progress with regard to Targets 6 to 13 which aimed to safeguard and reduce human impacts on biodiversity.
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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 | 0.096 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".