Biodiversity futures: digital approaches to knowledge and conservation of biological diversity
Bibliographic record
Abstract
Biodiversity, encompassing species diversity, genetic resources, and ecosystems, is essential for human well-being and quality of life. However, the scale of human activities has significantly impacted the planet's biodiversity, with many species facing extinction in the coming decades with unknown consequences. Global commitments, such as the Aichi Biodiversity Targets and the United Nations (UN) Sustainable Development Goals, are not delivering consistent results, and progress on conservation has been frustratingly slow. With a short time frame to meet the 2030 targets of the Kunming-Montreal Global Biodiversity Framework, urgent action is needed to address the crisis. Digital technologies emerge as indispensable tools in understanding, monitoring, and conserving biodiversity. They offer multiple solutions, from remote sensing to citizens involvement mediated by science apps, providing unprecedented volumes of data and innovative tools for conservation efforts. Despite their immense potential, digital solutions raise concerns about technology and data accessibility, environmental impacts, and technical limitations, as well as the need for specialized human resources, robust collaboration networks, and effective communication strategies. This paper, drawn from discussions at the Digital with Purpose Global Summit in 2023 and 2024, held in Portugal, and complemented by expert opinion and literature, reflects on existing biodiversity-related digital technologies, identifies challenges and opportunities, and proposes steps to strengthen the nexus between technology and the biodiversity agenda. By providing science and technology stakeholders with recommendations on accelerating the role of digital technologies in biodiversity knowledge and conservation, it aims to catalyse impactful change in this critical field of devising brighter futures for biodiversity and humanity.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".