SOOSmap: Your gateway to Antarctic data discovery
Bibliographic record
Abstract
The Southern Ocean is central to global ocean mixing and climate regulation via its disproportionate uptake of human-induced heat and carbon dioxide, yet the underlying processes are still poorly understood. Coordinated and sustained effort in observation and modelling of Southern Ocean processes in the past, present and future is therefore critical for understanding and mitigating the changes underway. Free and equitable access to Southern Ocean data is a fundamental prerequisite to meeting this objective. Here, we present a tool for discovery of, and access to, existing Southern Ocean data—SOOSmap, Version 2 (soosmap.aq). SOOSmap is a gateway to physical, biogeochemical and biological open-access data, free for anyone to use, from ocean science experts to classroom students. SOOSmap was developed in a collaboration of the Southern Ocean Observing System and the European Marine Observations and Data Network Physics project, with the aim to provide an easy to use one-stop-shop for Southern Ocean data held in repositories around the world. In this article, we illustrate the different methods of data access within SOOSmap, describe SOOSmap in the context of other polar data resources and initiatives, demonstrate how SOOSmap can be put into practice by a variety of stakeholders, and instruct users on how they can get involved in the SOOS community and contribute new data to SOOSmap, which is fundamental for this tool to continue to be useful for informing policy and decision-making about changes occurring in the Southern Ocean.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.046 |
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".