Bibliographic record
Abstract
Abstract Astronomy has an enviable wealth of historical observations. Some verge on the archaeological, and display rare events such as novæ and supernovæ; others range up to 100 or more years in age, and bear unique information about events that will never repeat in detail. Yet most astronomers today know little of those resources and the scientific potential which they harbour, so rather infrequent use is made today of those historical data. The problem is that historical data were perforce obtained in analogue formats, and because of those formats the data too tend to be regarded as hailing from a culture whose scientific significance is passé. But the medium is not the message! Astronomy’s archives of photographic observations constitute an irreplaceable resource. The change in technology from analogue to electronic recording in the late 20th Century was abrupt, and it left most of today’s astronomers unable to handle and use photographic data, and led to a general skepticism of the value of photographic observations for present-day studies of variability in the cosmos. But that is precisely what older data can do; in particular, the older the data the more reliable the base-line against which one can measure new trends, refine orbital parameters, discern period modulations, etc.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".