Preparing a 20µm Water Vapour Monitor (IRMA) for Operations at Dome C
Bibliographic record
Abstract
The Infrared Radiometer for Millimetre Astronomy (IRMA) is a compact, relatively low cost, 20 µm water vapour monitor. By carefully choosing a 2 µm band that contains only water vapour molecule transitions it is possible to use a simple infrared detector chip to measure the total flux emitted by a column of atmosphere and hence, via an atmospheric model, to determine the total precipitable water vapour. Since February 2005, an IRMA has been measuring precipitable water vapour levels in Chile at the Gemini South site on Cerro Pachon with a second unit added at the nearby Las Campanas observatories site in August 2005. In early 2006 data collection started with three new build IRMA units at three locations for the Thirty Meter Telescope (TMT) project site testing effort. Additionally, an IRMA unit is in the process of being modified to prepare it for operations at Dome C in Antarctica as an addition to the suite of instruments on the University of New South Wales' AASTINO site monitoring facility. We present here a description of the features of the TMT IRMA units that enable them to run in a remote, unattended location in the Chilean desert that are relevant to the similarly remote Dome C operations. In addition we describe the modifications that have been undertaken and that are currently being tested in order to enable the units to operate with minimal redesign at the extremely low Antarctic winter temperatures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".