Assessing the impact of observations on ocean forecasts and reanalyses: Part 1, Global studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Under GODAE OceanView the operational ocean modelling community has developed a suite of global ocean forecast, reanalysis and analysis systems. Each system has a critical dependence on ocean observations routinely assimilating observations of <i>in-situ</i> temperature and salinity, and satellite sea-level anomaly and sea surface temperature. This paper demonstrates the value and impact of ocean observations to three global eddy-permitting forecast systems, one global eddy-permitting model-independent analysis system, one eddy-resolving reanalysis system, and two seasonal prediction systems. All systems have been used to assess the impact of Argo profiles, including scenarios with no Argo data, and a degraded Argo array unanimously concluding that Argo is a critical data set the most critical for seasonal prediction, and as critical as satellite altimetry for eddy-permitting applications. Most systems show that TAO data are as important as Argo in the tropical Pacific, and that XBT data have an impact that is comparable to other data types in the vicinity of XBT transects. It is clear that no currently available data type is redundant. On the contrary, the components of the global ocean observing system complement each other remarkably well, providing sufficient information to monitor and forecast the global ocean.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it