Bibliographic record
Abstract
Abstract. Despite being considered one of the most pressing global issues, biodiversity loss and the degradation of ecosystems is continuing at an alarming rate. In December 2022, COP15 saw the adoption of the Kunming-Montreal Global Biodiversity Framework, where four overarching international goals for biodiversity and 23 targets. While these targets are a positive step to address the drivers of biodiversity loss, we will not only need public and political will to reach the goals and targets outlined but also more effective methods to integrate and use scientific information. To facilitate this, scientists and research institutions need to establish new and innovative approaches to transform the way science is conducted, communicated, and integrated into the policymaking process. This will require the scientific community to become proficient at working in inter and transdisciplinary teams, establishing connectivity, and engaging in the policymaking process to ensure that the best available scientific evidence is not only comprehensible to decision makers, but also timely and relevant. Here, we detail how scientists can embrace transformative change within and outside of their own communities to increase the impact of their research and help reach global targets that benefit society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.044 | 0.030 |
| Insufficient payload (model declined to judge) | 0.086 | 0.078 |
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".