Ethical recommendations for ocean observation
Bibliographic record
Abstract
Abstract. The United Nations proclaimed a decade of marine science for sustainable development (2021–2030) to develop a common framework that will ensure that ocean science can fully support countries in achieving the goal of sustainable development. Marine scientific understanding is fundamental to managing human activities that affect this environment, and ocean observations have a particularly important role in enhancing the knowledge base of our oceans. With this important task, scientists have the responsibility to act in an ethical way and apply all the fundamental principles described in the Cape Town statement: (a) ethical values, (b) social values and (c) cultural values (Peppoloni and Di Capua, 2017).This article is a fist attempt to highlight the core values applicable to ocean observation, which can then be improved and adopted as part of geoethics and the stewardship of the Earth system. It opens up avenues for reflection on geoethical implications in the field of ocean observation and suggests nine key principles that marine scientists could follow in their innovative research regarding open access to data, effectiveness, compliance with laws, environmental respect and nature conservation, reciprocal relation and cultural respect, equity and fairness, knowledge transfer, governance adapted to socio-ecological systems, and the use of animals in research.
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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 | 0.096 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.027 | 0.034 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".