Integrating blue: How do we make nationally determined contributions work for both blue carbon and local coastal communities?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Blue Carbon Ecosystems (BCEs) help mitigate and adapt to climate change but their integration into policy, such as Nationally Determined Contributions (NDCs), remains underdeveloped. Most BCE conservation requires community engagement, hence community-scale projects must be nested within the implementation of NDCs without compromising livelihoods or social justice. Thirty-three experts, drawn from academia, project development and policy, each developed ten key questions for consideration on how to achieve this. These questions were distilled into ten themes, ranked in order of importance, giving three broad categories of people, policy & finance, and science & technology. Critical considerations for success include the need for genuine participation by communities, inclusive project governance, integration of local work into national policies and practices, sustaining livelihoods and income (for example through the voluntary carbon market and/or national Payment for Ecosystem Services and other types of financial compensation schemes) and simplification of carbon accounting and verification methodologies to lower barriers to entry.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it