How do we achieve nature positive? A vision and targets for the UK residential and commercial development sector
Bibliographic record
Abstract
Abstract The Kunming-Montreal Global Biodiversity Framework’s 2050 Vision depicts a world living in harmony with nature where “biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. To achieve this vision, alternatives to business-as-usual are urgently needed, especially in the highest impacting sectors. Here we demonstrate the use of visioning and target setting to create an actionable roadmap to a ‘nature positive’ future for the UK residential and commercial development sector. During an online workshop, ten expert participants defined a shared vision for the development sector in 2050, and worked collaboratively to identify interim targets required to achieve that vision. The resulting roadmap highlights the need to improve biodiversity monitoring and assessment methods, strengthen Biodiversity Net Gain metrics, increase ecological literacy and conservation funding, and enhance community access to, and connection with, nature.
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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".