Bibliographic record
Abstract
The Subsidiary Body on Scientific, Technical and Technological Advice held its nineteenth meeting on 2–5 November in Montreal, Canada. Several recommendations from the meeting relate to coastal management. Recommendation XIX/7 on climate-related geo-engineering noted that the fifth Assessment Report of the Intergovernmental Panel on Climate Change did not consider the impacts of climate-related geo-engineering techniques on biodiversity and ecosystems. It also encouraged the thirteenth Conference of the Parties to the CBD in 2016 to adopt a decision promoting the use of a precautionary approach towards the use of climate-related geo-engineering techniques, emphasizing that the primary methods of addressing climate change are the reduction of greenhouse gas emissions at their sources and increasing the sinks that remove them. Contracting parties should promote ecosystem-based mitigation and adaptation techniques. Recommendation XIX/8 on forest biodiversity encouraged contracting parties to adopt a decision on forest policy related to the Aichi Biodiversity Targets. When developing and implementing forest policies in light of the Aichi Biodiversity Targets and other international agreements, parties should take into account climate change mitigation and adaptation and disaster risk reduction. Coastal forests, such as mangroves, play an important role in this regard.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.019 |
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".