Bibliographic record
Abstract
The Maunakea Spectroscopic Explorer shall be mainly devoted to perform deep, wide-field, spectroscopic surveys at spectral resolutions from ~2000 to ~20000, at visible and near-infrared wavelengths. Simultaneous spectral coverage at low resolution is required, while at high resolution only selected windows can be covered. Moreover, very high multiplexing (3200 objects) must be obtained at low resolution. At higher resolutions a decreased number of objects (~800) can be observed. To meet such high demanding requirements, a fiber-fed multi-object spectrograph concept has been designed by pupil-slicing the collimated beam, followed by multiple dispersive and camera optics. Different resolution modes are obtained by introducing anamorphic lenslets in front of the fiber arrays. The spectrograph is able to switch between three resolution modes (2000, 6500, 20000) by removing the anamorphic lenses and exchanging gratings. Camera lenses are fixed in place to increase stability. To enhance throughput, VPH first-order gratings has been preferred over echelle gratings. Moreover, throughput is kept high over all wavelength ranges by splitting light into more arms by dichroic beamsplitters and optimizing efficiency for each channel by proper selection of glass materials, coatings, and grating parameters.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".