The Smart Sea concept and its application for ocean management in a changing climate
Bibliographic record
Abstract
Global environmental change is a defining issue of our time. The ocean is a key component of the Earth system, and yet, in-depth understanding of its roles in sustaining life has not received the attention which it deserves. Humanity must develop a new relationship with the ocean characterized by protection, sustainable production, and prosperity. Society has too much to gain by implementing sustainability solutions and too much to lose by ignoring them. Our actions or inaction now will have far-reaching implications for future of all life on Earth. Ocean blueprint that calls for enforcing 30% Marine Protected Areas by 2030 requires real transformative action. This paper contains new ideas for combining the efforts of natural and social scientists, and traditional users of sea, and explores the potential of modern technologies to assist in this campaign. ‘Smart Sea’ concept introduced in this paper envisages synergies among the problem-solving approaches including digital tools, and eco-engineering and eco-mimicry solution options. Knowledge gaps have been highlighted and relevance of new knowledge systems emphasized together with enabling conditions to address the uncertainties associated with the ocean ecosystem. The ocean has a central position in actions towards preventing global warming of 1.5oC but measures to achieve it should consider that the ocean carbon sink is dynamic and is adversely affected when excessive carbon dioxide produces acidification. The selected measures are likely to have trade-offs, requiring analysis of multiple dimensions, for ensuring sustainable outcomes. The prevailing ocean health and urgency to mitigate it calls for combining global and local solutions, technologies and actions driven by safe and innovative solutions, and wherever possible, based on proof-of-concept. Deviating from the on-going incremental data collection systems to new forms of data-sharing using modern technological tools will contribute to addressing the glaring vacuum in knowledge of the ocean and facilitating a concerted global action for maintaining its ecosystem services. An attempt has been made in this paper to consolidate different opinions and experiences in moving from generalities to specifics for sustainable solutions that support economies, food security and the society.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".