A sustainable ocean for all
Bibliographic record
Abstract
Welcome to the opening editorial of npj Ocean Sustainability . This new interdisciplinary journal aims to provide a unique forum for sharing research, critically debating issues, and advancing practical solutions to achieve ocean sustainability. The ocean and people are deeply interconnected. Thus, decision-makers require integrative, interdisciplinary, and transdisciplinary knowledge to design solutions and approaches based on the multitude of visions for what a sustainable ocean entails. For that reason, the journal recognizes the benefits of knowledge pluralism and equally welcomes research from natural and social sciences; from marine ecology to Indigenous Studies; from the legal, policy, and management sciences to medical sciences, to arts and humanities. We acknowledge the fundamental need to understand and integrate the environmental and human dimensions into ocean research and management to effectively ensure long-term sustainable ocean use and conservation. We also acknowledge that while the ocean is “one” from a biophysical standpoint, there is a “plurality” of values and relationships between humans and the ocean, emerging from multiple geographical and historical specificities that need to be accounted for. Credit: Vasco Pissarra
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.048 | 0.024 |
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".