Greinargerð í kjölfar vinnustofu Biodice um líffræðilega fjölbreytni þann 23. apríl 2024.
Bibliographic record
Abstract
On April 23, 2024, the collaborative platform Biodice and its partners held a full-day workshop on biodiversity. The aim of the workshop was to analyse and discuss the issue of biodiversity as it appears in Icelandic administration. This workshop was a part of The Nordic Biodiversity Framework project (NBF), which was funded by the Nordic working group for Biodiversity of the Nordic Council of Ministers. The workshop was very well attended by about 40 people, all of which were representatives of ministries, governmental agencies and other various entities that work with biodiversity issues at the highest level of administration. The program consisted of presentations in the morning and group work in the afternoon. The purpose of the group work was, first, to connect people across the administration and, second, to share information about the current state of biodiversity issues regarding the Kunming-Montreal Biodiversity Framework (GBF). The group work results suggest that there is generally limited understanding of biodiversity in administration, except where life science experts are involved. All groups agreed on the importance of engaging stakeholders at all stages of the discussion. There was consensus on issues such as administration, climate, planning, information, funding, and prioritization. Participants recommended integrating biodiversity into all areas of administration and strengthening cooperation across ministries and institutions. It is crucial to ensure climate actions do not harm biodiversity, and planning decisions should benefit both. Biodiversity knowledge and information should be accessible to all, and education and awareness are key to implementing the GBF. Biodiversity must be prioritized and actions and policies need proper funding. Participants raised concerns about the disconnect between humans and nature, how local ownership and proximity in small municipalities can affect decision-making, the lack of accountability for the issue and new public health threats. Other ideas included establishing a Biodiversity Council, similar to the Climate Council, putting more focus on public health, disease prevention, and the importance of international cooperation. Overall, there was satisfaction with the workshop, and its results help assess Iceland’s progress in implementing the Global Biodiversity Framework (GBF).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.427 | 0.210 |
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".