Modeling of in support of long-term pathways and EU policies for bending the curve of biodiversity loss
Bibliographic record
Abstract
Reversing the declines in biodiversity trends is a widely adopted goal, reflected in both the Kunming-Montreal Global Biodiversity Framework, and the EU 2030 Biodiversity Strategy. In this presentation, we will show two examples of how models and scenarios can be mobilized to provide support to achieving these goals in the context of the broader sustainable agenda. In a first example, multiple economic and biodiversity models are used to assess long-term, global scale, pathways aiming to explore whether—and how—humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion reverse global biodiversity losses (Leclere et al, 2020). The results show that i) immediate efforts of unprecedented ambition and coordination could enable reversing the global terrestrial biodiversity trends caused by habitat conversion, and ii) that an integrated approach, combining increased protection and restoration efforts with sustainable production and consumption measures, is essential to not only enable a bending of global biodiversity trends before 2050, but also limit trade-offs and harness synergies with other sustainable goals. In a second example, we will demonstrate how models and scenarios are also mobilized to support policy design at the EU scale, with an application focusing on assessing the land use, LULUCF emissions and biodiversity implications of EU climate (e.g., Fitfor55 package and LULUCF regulation) and biodiversity (e.g., Nature Restoration Law) and their interactions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".