Design and deployment of the Bonne Bay Observatory (B20)
Bibliographic record
Abstract
We have deployed the Bonne Bay Observatory (B20) in Bonne Bay, a fjord on the west coast of Newfoundland, in the spring of 2004. The scientific goal of this system is to provide continuous, year-round, real-time data to enhance our understanding of the coupling between the physical and biological environment in this sub-Arctic fjord, which is ice covered for several months each year. The Observatory permits investigators to schedule and interactively manage real time data acquisition and control of the network of sensors. Serving a multidisciplinary team, the instrument array is diverse, including acoustic sensors to determine currents, bubble distribution and plankton abundance, video to determine plankton and benthos species abundance, and sensors for temperature, salinity, chlorophyll fluorescence, carbon dioxide, oxygen, inorganic nutrient concentrations and spectral irradiance. Instruments are deployed on fixed and moveable structures and on an underwater profiling winch. The control and telemetry system includes a power distribution sub-system and TCP/IP based network consisting of two local area networks, one on shore linking data acquisition and control computers and one underwater connecting the sensors, joined by an armored 1.4 km electro-optic cable. The cable provides up to two 100BASE-FX network connections and 2 kW of power to the underwater systems. The system is designed to operate autonomously, and to be controlled remotely by the DACNet ocean observatory operating system. The underwater hardware elements are modular, accommodating guest instruments at spare Ethernet and serial ports. The paper describes the system design with description of instrumentation deployed underwater for the first time, lessons learned during design and deployment and presents preliminary samples of the data collected.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".