Stop neglecting the blue! The relevance of One Ocean Health for the planet we want
Bibliographic record
Abstract
Abstract One Health, Planetary Health, and EcoHealth – there are myriad conceptual frameworks for expanding discussions to move beyond human, animal and plant health, to be inclusive for the health of ecosystems. At present, however, despite its crucial role in the survival of our world, the ocean receives insufficient consideration within these frameworks. Therefore, greater emphasis should be placed on what can be coined One Ocean Health . To this end, we relate the health of the ocean to currently dominant connotations of the concept of health and acknowledge the ocean’s nature as one gigantic, interconnected ecosystem; a common, irreplaceable ocean on which all living things depend, human and non-human, terrestrial and aquatic alike. Recognizing the interconnectedness of ocean and society, and drawing on concepts from medical theory, we advocate for a holistic, co-developed approach to ocean health that integrates not only scientific and policy perspectives but also acknowledges the cultural diversity in the ways in which people relate to the ocean and engage with it. To achieve this, objective and subjective perspectives of what constitutes “diseases” in marine ecosystems need to be considered, while also defining means and normative goals of a “healthy ocean” – always bearing in mind the fact that this is not an end in itself, but remains crucial for the preservation of the planet that we as humans need. One Health impact statement This research represents an interdisciplinary reflection on the neglected role of ocean health within the One Health framework. Despite its critical importance, the ocean is often rather absent from discussions from One Health deliberations. We examine how different conceptualizations of what constitutes a “healthy” or “diseased” system influence these debates, and explore the challenges and opportunities for integrating holistic, transdisciplinary views into ocean health via the proposed One Ocean Health perspective. We argue that this perspective further emphasizes the deep interconnectedness of ocean, human and non-human organisms, advocating for enhanced inter- and transdisciplinary collaboration and knowledge exchange. Recognizing the ocean as a fundamental component of global health is essential to addressing complex environmental and societal challenges, ultimately ensuring a more resilient and sustainable future.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".