The Data Acquisition and Control Network (DACNet) Ocean Observatory Operating System
Bibliographic record
Abstract
Networks of heterogeneous in-situ marine sensors pose significant challenges with regard to data acquisition and control. Scientific marine instruments, typically lacking standardized data formats and control protocols, are difficult to cohesively integrate in large-scale networked observing systems. The result is often a patchwork system of vendor supplied desktop applications and custom developments that can become cost prohibitive and unsustainable with respect to manageability, maintenance, and extensibility. The DACNet (Data Acquisition and Control Network) ocean observatory operating system was conceived in 1999 as a scalable, modular solution for automated long-term data collection. The core element is a universal telemetry acquisition module that is configured through an extensible sensor description meta language to provide comprehensive acquisition and control services for virtually any sensing instrument. Acquired telemetry can be simultaneously streamed to multiple local or remote destinations. An open application programming interface based on the sensor meta language allows for deployment of dynamically loaded in-line processing plug-ins to support adaptive sampling, real time display, and event detection. Upper management functions, accessible through secure remote interfaces, enable scheduled control and systematic monitoring of science instruments, power infrastructure, and ancillary devices such as profiling winches. This paper gives an overview of the DACNet Ocean Observatory operating system design and it discusses how key features have met real-world requirements over five years of deployment in wireless and cabled applications in North America and Europe.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".