Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Mid-infrared ELT Imager and Spectrograph (METIS) is one of the four first-generation scientific instruments for the Extremely Large Telescope (ELT), funded for construction by ESO and designed and built by a consortium of research institutes, lead by NOVA in the Netherlands. The consortium consists of 12 partner institutes spread over Europe and includes the US, Taiwan plus ESO. METIS is designed to operate in the 3 to 13 µm wavelength range, and aims at both imaging, spectroscopy and coronagraphy. In November 2022, METIS had its main final design review (FDR), and the METIS sub-systems are now in the manufacturing, assembly, integration and test phase (MAIT), while the preparations for the system AIT phase in Leiden has started. The management of a project of this scale comes with its own challenges. The development of METIS is a project substantially bigger than instrument developments for the Very Large Telescope (VLT), but still smaller than most space missions. In addition, also the ELT as a project differs from its predecessor VLT. With the ELT and METIS both being new facilities of a new scale it comes with its own dynamics in management, change control, and systems engineering, in which we want to make use of the state-of-the-art methods, while still utilizing the heritage built up at the partner institutes. In this paper we present the management organization of METIS, both in terms of rolled-out processes, as well as the required areas of expertise, project phasing and staffing, and compare it with previous projects. We will focus on the various lessons learned from the design phase, and the plans for the pproject phases to come.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it