Curtailing the collapse of the living world
Bibliographic record
Abstract
On 7 December of this year, the fate of the entire living world will be determined in Montreal, Canada, at the 15th Conference of the Parties (COP 15) of the UN Convention on Biological Diversity (CBD). If this triggers a sense of déjà vu, then that is because no more than a couple of weeks ago, we experienced a massive media blitz covering a different COP. That was COP 27, or the 27th Conference of the Parties of the UN Framework Convention on Climate Change (FCCC), held in Sharm el-Sheikh, Egypt. The timing is unfortunate because COP 15 of the CBD is vastly more important than COP 27 of the FCCC. In a century marked by horrific environmental crises, from mass extinction to emerging diseases to invasive species, all of which are anthropogenic and global in scale, climate change has been the main attention getter. Given the political and economic concerns surrounding fossil fuels and greenhouse gas emissions from industry and agriculture, it is perhaps not surprising that climate change dominates the global change agenda, but the CBD must take center stage. We say this because of the many dimensions of anthropogenic global change, the most critical, complex, and challenging of which is that of biodiversity loss.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".