Bibliographic record
Abstract
In December 2022, the COP15 for Biodiversity approved the Kunming-Montreal Global Biodiversity Framework, a protocol for the protection of the planetary ecosystems that complements the Paris Agreement on Climate Change with the aim of preventing the collapse of the biosphere. A global network of areas with varying degrees of naturality and artificiality, capable of halting the loss of biodiversity and reducing the concentration of carbon dioxide in the atmosphere, will extend over 30% of the Earth by 2030 and be further consolidated by 2050. The framework raises a multiplicity of issues engaged by the landscape project: the conservation of species and ecosystems of the biosphere; the environmental rehabilitation of degraded terrestrial and marine areas; the equitable management of ancestral lands and the rights of indigenous peoples; the protection of cultural landscapes and the support for local communities; the abandonment of both the unsustainable exploitation of the territories, as well as their musealisation and vernacularisation in the service of global tourism; the enhancement of the ecological contributions from degraded, exploited or underutilized areas, on the inhabited edges or in the operational hinterlands of planetary urbanization; the assisting of contemporary anthro-ecological systems towards new forms of equilibrium, conventionally defined by the terms of sustainability and resilience.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.172 | 0.065 |
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".