An assessment of hierarchical data fusion using SEABAR'07 data
Bibliographic record
Abstract
The results of processing selected runs from the SEABAR'07 multistatic sonar trials dataset through General Dynamics Canada's Multiple Target Tracker (MTT) hierarchical data fusion system are reported. The purpose of this exercise was to ascertain the performance potential of the MTT and, by inference, of hierarchical data fusion based tracking generally, against a real multistatic sonar scenario. Selected runs of the original SEABAR'07 dataset have proven themselves well suited to this purpose. Tracking results on these runs are quite positive. To compensate for the lack of a real target in these runs, the SEABAR'07 dataset also includes a modified version, in which the strong echo repeater returns have been replaced by much weaker returns computed using the BASIS bistatic target aspect model. Tracking results with this modified dataset proved less encouraging. These results suggest that the viability of multistatic sonar tracking using a hierarchical data fusion system like the MTT appears promising, but remains unproven; a calibrated trials dataset containing a real target is required to provide a definitive answer.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".