Bibliographic record
Abstract
The Ocean Network Seafloor Instrumentation (ONSFI) Project, which commenced in 2007, is a five year multidisciplinary research and development (R&D) project to design, fabricate and validate a proof-of-concept seafloor array of wireless marine sensors for use in monitoring seabed processes, including applications such as geological imaging and earthquake detection. Individual compact, low-cost sensors, called 'SEAformatics' pods, will be self-powered through ocean bottom currents and will be able to communicate with each other and to the Internet through surface master units to facilitate observation of the ocean floor from shore. The ONSFI project has four R&D streams: marine sensors; power generation; communication networking; and final integration of the systems into a working prototype. This project links sensor science, wireless communications technologies, smart materials, ocean current power harvesting technologies and web-based information technologies. The final phase of the project is to integrate the sensor, power and networking sub-projects with the unit's structural and deployment elements to result in an autonomous SEAformatics pod which is ready to be deployed and tested in a proof-of-concept sample array, in situ in a marine environment. Targeted completion date is the fall of 2011.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".