Astronomy’s Photographic Glass Plates: Demonstrating Value Through Use Cases
Bibliographic record
Abstract
Astronomy's extensive collections of photographic glass plates contain historical images and spectra of celestial objects, documenting more than a century of the observable cosmos. Many reveal changes, both sudden (explosive), periodic, or gradual (evolutionary), which is material of immense interest for time-domain studies because of the long time-base they cover. Those early photographic observations also furnished all the basic data which supported our early understanding of the universe, and from which modern stellar classifications have been derived. Once the ubiquitous workhorse detector, plates or film are now replaced by electronic detectors, and systems are modified to take advantage of advances in telescope technology. This change poses challenges of preservation and accessibility for the plates, leading administrators to question the usefulness of the older materials in relation to the cost of their care and preservation. The following paper details many examples of reusing or re-purposing those plates, demonstrates their unique value to modern astronomy and the history of science, and makes a strong case for committing resources towards their long-term preservation and ultimately their comprehensive digitization.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".