Science enabled by ocean observatory acoustics: The NSF ORION program
Bibliographic record
Abstract
The National Science Foundation (NSF) has started the Ocean Research Interactive Observatories Network (ORION) program for research-driven sustained observations. The core infrastructure will consist of: (1) a coarse global array of buoys, (2) a regional cabled observatory in the northeast Pacific (with Canada already funded for the northern portion), and (3) coastal observatories. Seafloor junction boxes providing power and communications are a common enabling feature. The ORION Workshop (Puerto Rico, 4–8 January 2004) developed science themes that can be addressed utilizing this infrastructure. Acoustics enable much of the science. The use of acoustics to sense the synoptic 3-D/volumetric ocean environment was found to be ubiquitous through most ORION working groups. One reason for this is the relative transparency of the ocean to sound and the opaqueness to electromagnetic radiation. Participants at the workshop formed an Acoustics Working Group. Based on its report, we review the science and technical drivers for acoustics and educational opportunities. Themes include inherent volumetric, near instantaneous sampling, robust transducers, imaging at many scales, navigation, communications, and using sound in the sea as a major education and outreach mechanism. Recommendations include the formation of a standing ORION committee on acoustics and a workshop. See http://www.orionprogram.org and http://www.oce.uri.edu/ao/AOWEBPAGE.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".