Observing <scp>S</scp>ystem <scp>S</scp>imulation <scp>E</scp>xperiments for an array of autonomous biogeochemical profiling floats in the <scp>S</scp>outhern <scp>O</scp>cean
Bibliographic record
Abstract
Abstract This study uses Observing System Simulation Experiments (OSSEs) to examine the reconstruction of biogeochemical variables in the Southern Ocean from an array of autonomous profiling floats. In these OSSEs, designed to be relevant to the Southern Ocean Carbon and Climate Observation and Modeling (SOCCOM) project, the simulated floats move with oceanic currents and sample dissolved oxygen and inorganic carbon. The annual mean and seasonal cycle of these fields are then reconstructed and compared to the original model fields. The reconstruction skill is quantified with the reconstruction error (RErr), defined as the difference between the reconstructed and actual model fields, weighted by a local measure of the spatiotemporal variability. The square of the RErr is small (<0.5) for 150 floats in most of the domain, which is interpreted to mean that the reconstruction skill is high. An idealized analytical study demonstrates that the RErr depends on the magnitude of the seasonal cycle, spatial gradients, speed of float movement, amplitude of mesoscale variability, and number of floats. These factors explain a large part of the spatial variability in the RErr and can be used to predict the reconstruction skill of the SOCCOM array. Furthermore, our results demonstrate that an array size of 150 floats is a reasonable choice for reconstruction of surface properties and annual‐mean 2000 m inventories, with the exception of the seasonal cycle in parts of the Indo‐Atlantic, and that doubling this number to 300 results in a very modest increase in the reconstruction skill for dissolved oxygen.
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How this classification was reachedexpand
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".