The Expanded Very Large Array: goals, progress, and plans
Bibliographic record
Abstract
The Expanded Very Large Array project has the top-level goal of enhancing the performance of the Very Large Array by an order of magnitude or more in all areas: sensitivity, frequency coverge, spectral resolution, and spatial resolution. The project is being implemented in two, overlapping phases: Phase I, which began in 2000 and will finish by 2012 addresses all new capabilities except spatial resolution, and Phase II, which will improve tenfold the spatial resolution, and which is planned to begin in 2006, and finish by 2013. Progress in Phase I is very good, with first light and first fringes having been achieved, and tests of the new hardware and software now underway. A proposal for funding Phase II has now been delivered to the National Science Foundation. A critical component of the project is the new correlator, being designed and built by the Canadian Herzberg Institute of Astrophysics at the DRAO in Penticton, BC Canada. This new advanced correlator will be delivered beginning in late 2008. First shared-risk science with the early portions of the correlator will be done in late 2007.
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 | 0.024 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".