Preparing the New Phase of Argo: Scientific Achievements of the NAOS Project
Bibliographic record
Abstract
Argo, the international array of profiling floats, is a major component of the global ocean and climate observing system. In 2010, the NAOS (Novel Argo Observing System) project was selected as part of the French “Investissements d’Avenir” Equipex program. The objectives of NAOS were to consolidate the French contribution to Argo’s core mission (global temperature and salinity measurements down to 2000 m), and also to develop the future generation of French Argo profiling floats and prepare the next phase of the Argo program with an extension to the deep ocean (Deep Argo), biogeochemistry (BGC-Argo) and polar seas. This paper summarizes how NAOS has met its objectives. The project significantly boosted France’s contribution to Argo’s core mission by deploying more than 100 NAOS standard Argo profiling floats. In addition, NAOS deployed new-generation floats as part of three scientific experiments: biogeochemical floats in the Mediterranean Sea, biogeochemical floats in the Arctic Ocean, and deep floats with oxygen sensors in the North Atlantic. The experiment in the Mediterranean Sea, launched in 2012, implemented and maintained a network of BGC-Argo floats at basin scale for the first time. The 32 BGC-Argo floats deployed and about 4000 BGC profiles collected have vastly improved characterization of the biogeochemical and ecosystem dynamics of the Mediterranean. Meanwhile, experiments in the Arctic and in the North Atlantic, starting in 2015 and deploying 20 Arctic BGC floats and 23 deep floats, have provided unique observations on biogeochemical cycles in the Arctic and deep-water masses, as well as ocean circulation variability in the North Atlantic. NAOS has therefore paved the way to the new operational phase of the Argo program in France that includes BGC and Deep Argo extensions. The objectives and characteristics of this new phase of Argo-France are discussed in the conclusion.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".