Bibliographic record
Abstract
Fourier transform spectroscopy (FTS) is now well established as a powerful diagnostic tool in far-infrared and submillimetre spectroscopic applications. The high throughput of an FTS is of particular importance in this energy starved region of the electromagnetic spectrum. It has been recognized for many years that an FTS can be readily modified for imaging spectroscopic applications by simply placing a detector array at one of its outputs. The development of array detectors operating at optical wavelengths, which has been driven in part by the consumer market, has been impressive. Similar advances in the development of array detectors have occurred at longer wavelengths, with cost increasing monotonically with wavelength. In particular, recent advances in the production of large format, TES detector arrays (e.g. SCUBA-2) presents a new opportunity for imaging spectroscopic applications at submillimetre wavelengths. The underlying principles of imaging Fourier transform spectroscopy (iFTS) are reviewed, and the challenges facing this field discussed with respect to two iFTS systems (SPIRE and FTS-2) that have been developed for submillimetre astronomical applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".