Effects of the Pandemic on Observing the Global Ocean
Bibliographic record
Abstract
Abstract The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".