Bibliographic record
Abstract
Go submit a paper to the IEEE International Symposium on Antennas and Propagation and USNCIURSI National Radio Science Meeting, to be held July 3–8, 2011, in Spokane, Washington! Even if you don't submit a paper (the submission deadline is January 14, 20 11), plan on attending. In addition to all of the excellent technical reasons associated with what will be a great conference, another major reason to attend is because it is being held in Spokane. If you want to know the answer to the question, “Why Spokane,” read the “Welcome” from Jeff Young, the General Chair of the meeting this issue, and the pages that follow describing all there is to see and do in conjunction with this meeting. The real answer to this question is the location of Spokane. It is a gateway to some of the most beautiful country in North America: the Pacific Northwest. This is home to spectacular national forests and parks, making it an ideal place for family holidays. Glacier, Olympic, Mount Rainier, North Cascades, and Nez Perce National Parks in the US, and Banff and Jasper National Parks in Canada, are all within a six-hour drive or less. Even the hotel in which most attendees will be staying is considered worth the trip. This meeting is being held in a very special region that you shouldn't miss.
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 | 0.011 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.151 | 0.096 |
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".