Our Ocean, Our Future - Ocean Sciences Meeting
Bibliographic record
Abstract
Slides from the Ocean Sciences Meeting 2022 Keynote Address. As was discussed throughout the conference, the FLUIDITY of our ocean planet is our FUTURE. It is essentially what gives it life. And yet, as we know, our world today is simply, IN TROUBLE. It's safe to say that what we've done with environmental degradation and the issues of social instability are creating a significant amount of uncertainty for all of us, and then there is the unthinkable situation in Ukraine and with the associated crisis in the Black Sea. These issues are overwhelming. And somehow they're all interconnected — climate change, natural disasters, loss of nature in the ocean, overpopulation along the ocean. Addressing these issues with equitable SOLUTIONS is going to be challenging for all of us for the rest of our lives, requiring new governance, new policies, new market approaches, and certainly new technologies which is a focus of the talk. ONE MAJOR SOLUTION I would argue is GEOGRAPHIC, where a so-called Geographic Approach can come to the rescue to help us organize and optimize our knowledge of the ocean (ALL OF OUR KNOWLEDGE, FROM ALL CULTURES, and GEOGRAPHIES) to solve these challenges and to SAVE it. And what I mean by a Geographic Approach is really a Way of Thinking and Problem Solving … … that Integrates spatial data Science & Information Into How We Explore, Designate, Understand, Manage and Communicate about the ocean This approach integrates and supports powerful methodologies: geoanalytics, creating insights and understanding; geovisualization, a language through maps and visualization for communicating the content and the context of our world; geodesign, designing sustainable and inclusive futures; geocollaboration, engaging all the community; and geoaccounting, being able to account for all the factors, setting up balanced measures that are driven by our shared value of the ocean, our knowledge, climate financing and much more. https://www.aslo.org/osm2022/plenary-speakers/
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.443 | 0.279 |
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".