The Digitization of Photographic Spectra in the Dominion Astrophysical Observatory Plate Collection with Commercial Scanners: A Pilot Study
Bibliographic record
Abstract
Abstract Commercial flatbed scanners have the potential to deliver a quick and efficient means of capturing the scientific content of spectra recorded on photographic plates. We discuss the digitization of selected spectra in the Dominion Astrophysical Observatory (DAO) photographic plate collection with commercial scanners. In this pilot study, emphasis is placed on assessing if the information on the plates can be recovered using Epson V800 and 12000XL scanners; the more complicated issues associated with the shortcomings of photographic materials, such as correcting for nonlinearity, are deferred to a future study. Spectra of Vega ( α Lyr) that were recorded over ∼4 decades with the DAO 1.8 m telescope are examined. These spectra sample a range of photographic emulsions, plate preparation techniques, calibration information, observing techniques, and spectrograph configuration. A scanning density of 2400 elements per inch recovers information in the spectra. Differences in the modulation transfer function (MTF) of the two scanners are found, with the Epson 12000XL having a superior MTF. Comparisons with a CCD spectrum of Vega confirm that moderately weak features are faithfully recovered in photographic spectra that have been digitized with the 12000XL scanner. The importance of scanning the full plate to cover the light profile of the target and calibration information is emphasized. Lessons learned from these experiments are also presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".