Bibliographic record
Abstract
For the past 10 years Dr. J. B. Oke has served the PASP and the community as the Associate Editor for Instrumentation. For personal reasons he has asked to step down, and in consultation with him we sought a new PASP Associate Editor for Instrumentation. We are delighted to announce that the ASP Board of Directors has endorsed the appointment of two extremely wellqualified astronomers to share the responsibility of this position. They are Dr. Daniel Fabricant of the Harvard-Smithsonian Center for Astrophysics and Dr. Harland Epps of UCO/Lick Observatory, University of California, Santa Cruz. Both have been involved in numerous instrumentation projects and have also been regular contributors to the PASP. The transition to the new Associate Editors for Instrumentation will begin immediately. Dr. Oke has agreed to continue to handle the manuscripts he is now working with, but all new instrumentation papers will be transferred to the new editors. Authors should continue to submit their astronomical instrumentation papers to the PASP ftp site in Victoria (see the instructions at http://pasp.phys.uvic.ca). Similarly, any hardcopy manuscripts should be sent to the main office in Victoria:
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.139 | 0.182 |
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".