Ocean Observing System Instrument Network Infrastructure
Bibliographic record
Abstract
We summarize results of a Workshop on Instrument Software Infrastructure held at MBARI, Moss Landing, California USA from September 13-15, 2004, jointly sponsored the National Science Foundation (NSF) and Ocean Research Interactive Observatory Networks (ORION) program. The Workshop included over fifty participants, including international participants from Germany, Canada, and Japan. This was one of the first technical workshops in the development of a series of ocean observatories under the US Ocean Observatory Initiative (OOI) being managed under the ORION program. The specific focus of this workshop was to define the standard requirements to be met by software infrastructure for sensors, instruments and platforms for observing systems in the ORION program. These requirements include the issues of configuring, interfacing, and managing devices, including sensors and actuators, to a networked based observing system as well as managing the resources necessary to support such devices. The topics include the capability of supporting plug-and-work instrumentation using service oriented network architecture. A major issue addressed is the observatory infrastructure requirements necessary for managing data and metadata coming from sensors and instruments of the observatory in support of an integrated data management system
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".