Good fisheries management is good carbon management
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
Abstract Climate change is causing persistent, widespread, and significant impacts on marine ecosystems which are predicted to interact and intensify. Overfishing and associated habitat degradation have put many fish populations and marine ecosystems at risk and is making the ocean more vulnerable to climate change and less capable of buffering against its effects. In this Perspective, we review how overfishing is disrupting the important role of marine vertebrates in the ocean carbon cycle, causing disturbance and damage to the carbon-rich seabed, and contributing to rising greenhouse gas emissions through fuel use. We discuss how implementing good fisheries management can reduce or remove many of the impacts associated with overfishing, including fish stock collapse, destruction of seabed habitats, provision of harmful subsidies and accompanying socio-economic impacts. Managing overfishing is one of the most effective strategies in protecting ocean carbon stores and can make an important contribution to climate mitigation and adaptation.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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