Bibliographic record
Abstract
UNEP - World Congress on Justice, Governance and Law for Environmental Sustainability (Soledad Aguilar) 204 UN / GA 67th Session - Preparations for Upcoming Session 206 UNCSD / Rio+20 - Contribution to the Development of Environmental Law (Eckard Rehbinder) 210 UNCSD / Rio+20 - Reflecting on Measured Deliberations (Nicholas A. Robinson) 219 UNCSD / Rio+20 - From Stockholm to Rio: Quo vadis? (Sheila Abed) 232 UNCSD / Rio+20 - A New Beginning for Climate Negotiations? (Mukul Sanwal) 234 UNCSD / Rio+20 - Reflections on Oceans and SIDS (Ann Powers) 239 CBD / SBSTTA-16/ WGRI-4 - Development on Marine Biodiversity and on Climate Change (Elisa Morgera) 240 CBD / ICNP-2 - Emerging Legal Questions (Elisa Morgera) 244 WIPO / IGC - Review of 2012 Sessions (Elsa Tsioumani) 246 Ramsar Convention / CBD - Addressing Climate Change (Amado Tolentino) 248 Ramsar Convention / COP-11 - Institutional Hosting "Controversy Laid to Rest?" 251 Montreal Protocol / OEWG-32 - Preparations for 25th Anniversary MOP 253 CITES / SC-62 - Tackling the Elephant Crisis (Elisa Morgera) 254 FAO / ITPGRFA - The Second High-Level Roundtable (Elsa Tsioumani) 258 Mercury INC-4 - Crafting a Package Deal (Julio Barboza) 260 Mercury INC-4 - Considering Breadth and Depth - Soledad Aguilar 263 ITLOS - Bay of Bengal Delimitation Decision 265
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.355 | 0.215 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".