Bibliographic record
Abstract
Georgina Mace proposed bending the curve of biodiversity loss as a fitting ambition for the Convention on Biological Diversity. The new Global Biodiversity Monitoring Framework (GBMF) may increase the chances of meeting the goals and targets in the Kunming-Montreal Global Biodiversity Framework (KMGBF), which requires bending the curve. To meet the outcome goals of KMGBF, the GBMF should support adaptive policy responses to the state of biodiversity, which in turn requires a 'satnav' for nature. The twin pillars of such a satnav are (i) models to predict expected future outcomes of today's choices; and (ii) rapid feedback from monitoring to enable course corrections and model improvement. These same elements will also empower organizations to ensure that their actions are truly nature-positive, but they are not yet written into the GBMF. Without a satnav, society will effectively have to try to find its way to the outcome goals by looking in the rear-view mirror that the current headline indicators provide. Drawing contrasts and parallels with climate modelling, I discuss challenges for indicators, models, data and research culture that must be overcome if we are to bend the curve, and suggest ways forward.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".