Perspectives from Developers and Users of the GOA-ON in a Box Kit: A Model for Capacity Sharing in Ocean Sciences
Bibliographic record
Abstract
Providing reliable instrumentation that enables collection of high-quality, comparable data is one of the most challenging aspects of establishing ocean acidification monitoring programs. This is especially true for under-resourced countries, where such data are mostly unavailable. In 2016, The Ocean Foundation (TOF) worked with international bodies, including the International Atomic Energy Agency’s Ocean Acidification International Coordination Centre and the Global Ocean Acidification Observing Network (GOA-ON), and subject matter experts to develop a set of equipment known as the “GOA-ON in a Box” kit (The Ocean Foundation, 2017). This comprehensive kit provides researchers with everything needed—down to specialized rubber bands—to obtain weather-quality carbonate system measurements as defined by GOA-ON (Newton et al., 2015). Data are generated from spectrophotometric measurements of pHT and manual titrations for total alkalinity from discrete samples as well as in situ sensors, the iSAMI-pH and a CTD. The kit’s modular design, composed of nearly 100 unique items, makes it much less expensive than comparable integrated systems and allows for easier troubleshooting and replacement of supplied spare components.
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.
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 | 0.089 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".