Overview of the CHILL-ICE 2021 Science Experiments and Research Campaign
Bibliographic record
Abstract
The main objective of the CHILL-ICE (Construction of a Habitat Inside a Lunar-analogue Lava tube—Iceland; a campaign by ICEE Space and EuroMoonMars) prototype mission was to deploy an inflatable habitat and its systems inside a lunar analogue lava tube. This took place during an 8-hour extra vehicular activity (EVA) with three analogue astronauts as part of a three-day mission. CHILL-ICE 2021 was carried out in July/August 2021 and consisted of two missions and was accomplished through successful collaboration of nonprofit research organizations, agencies, companies, and universities across 16 nations. The pilot campaign successfully reached its main objective: the testing of emergency equipment designed to help astronauts survive when first arriving to a new celestial body and to perform experiments similar to those that would be carried out off-planet. This article is a review of the scientific research experiments carried out during and after the mission: SpotNet, an artificial intelligence (AI) astronaut detection vision system; training for studies of the geological surroundings examined during EVAs; astronaut vigilance experiments carried out before, during, and after the mission; and Lunar Zebro, a legged rover intended to assist the crew in traversing and exploring harsh terrain.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".