Hydroacoustic Studies Using HydroCAM - Station-centric Integration of Models and Observations Quarterly Report No.4 July 2003 - September 2003
Bibliographic record
Abstract
OAK B272 Quarterly technical report summarizing BBN's efforts to improve DOE's hydroacoustic modeling and analysis capability for nuclear explosion monitoring. BBN's work during the third quarter of 2003 was focused on preparations for and participation in the 2003 Seismic Research Review Meeting, unit testing and bug fixes to HydroCAM 4.1, data collection and analysis, and procuring high-resolution bathymetric data. In an attempt to save money, BBN scaled back its labor in the third quarter, delaying some deliverables but saving contract funding in case our next increment is delayed. We have succeeded in finding the correct Naval contact that can help us procure high-resolution bathymetry data. Although these data may require the release of a classified version of HydroCAM, we are optimistic that we will be able to acquire and integrate high-resolution bathymetric data near the Indian Ocean IMS stations. HydroCAM 4.1, which includes the ability to make blockage predictions using varying resolution bathymetric data, has completed unit testing and is now under integration (release) testing. We hope to deliver that functionality to DOE and AFTAC in November. BBN improved its database of hydroacoustic events in the Indian Ocean by including meta-data for associated arrivals. For each earthquake event, BBN is now picking the direct arrival at each station (Diego Garcia North and South, and Cape Leeuwin) and associating that arrival with the origin information that we are compiling. The data for 2001, 2002 and 2003 (to date) will be delivered to LLNL for integration into the Knowledge Base during the fourth quarter of 2003.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".