OceanGliders: A Component of the Integrated GOOS
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
The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintain and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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