Remote visualisation of Labrador convection in large oceanic datasets
Bibliographic record
Abstract
Abstract The oceans relinquish O(1PW) of heat into the atmosphere at high latitudes, the lion's share of which originates in localised ‘hotspots’ of violent convective mixing, but despite their small horizontal scale—O(10–100km)—these features may penetrate deeply into the thermocline and are vital in maintaining the Atlantic Meridional Overturning Circulation (MOC). Accurate modelling of the MOC, therefore, requires a large‐scale numerical model with very fine resolution. The global high‐resolution ocean model, Ocean Circulation Climate Advanced Model (OCCAM) has been developed and run at the Southampton Oceanography Centre (SOC) for many years. It was configured to resolve the energetic scales of oceanic motions, and its output is stored at the Manchester Supercomputer Centre. Although this community resource represents a treasure trove of potential new insights into the nature of the world ocean, it remains relatively unexploited for a number of reasons, not the least of which is its sheer size. A system being developed at SOC under the auspices of the Grid for Ocean Diagnostics, Interactive Visualisation and Analysis (GODIVA) project makes the remote visualisation of very large volumes of data on modest hardware (e.g. a laptop with no special graphics capability) a present reality. The GODIVA system is enabling the unresolved question of oceanic convection and its relationship to large‐scale flows to be investigated; a question that lies at the heart of many current climate change issues. In this article, one aspect of the GODIVA is presented, and used to locate and visualise regions of convective mixing in the OCCAM Labrador Sea. Copyright © 2006 Royal Meteorological 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".