Esa Caves: training astronauts for SPACE exploration
Bibliographic record
Abstract
The first spaceflight was several decades ago, and yet extraterrestrial exploration is only at the beginning and has mainly\nbeen carried out by robotic probes and rovers sent to extraterrestrial planets and deep space. In the future human extraterrestrial\nexploration will take place and to get ready for long periods of permanence in space, astronauts are trained during long\nduration missions on the International Space Station (ISS). To prepare for such endeavours, team training activities are\nperformed in extreme environments on Earth, as isolated deserts, base camps on Antarctica, or stations built on the bottom\nof the sea, trying to simulate the conditions and operations of space. Space agencies are also particularly interested in the\nsearch of signs of life forms in past or present extreme natural environments, such as salt lakes in remote deserts, very deep\nocean habitats, submarine volcanic areas, sulphuric acid caves, and lava tubes. One natural environment that very realistically\nmimics an extraterrestrial exploration habitat is the cave. Caves are dark, remote places, with constant temperature, many\nlogistic problems and stressors (isolation, communication and supply difficulties, physical barriers), and their exploration\nrequires discipline, teamwork, technical skills and a great deal of behavioural adaptation. For this reason, since 2008 the\nEuropean Space Agency has carried out training activities in the subterranean environment and the CAVES project is one of\nthose training courses, probably the most realistic one. CAVES stands for Cooperative Adventure for Valuing and Exercising\nhuman behaviour and performance Skills, and is meant as a multidisciplinary multicultural team exploration mission in a\ncave. It has been developed by ESA in the past few years (2008–2011) and is open for training of astronauts of the ISS Partner\nSpace Agencies (USA, Russia, Japan, Canada, and Europe). Astronauts are first trained for 5 days to explore, document and\nsurvey a karst system, then take on a cave exploration mission for 6 days underground. A team of expert cave instructors, a\nHuman Behaviour and Performance facilitator, scientists and video reporters, ensure that all tasks are performed in complete\nsafety and guides all these astronauts’ activities. During the underground mission the astronauts’ technical competences are\nchallenged (exploring, surveying, taking pictures), their human behaviour and decision-making skills are debriefed, and they\nare required to carry out an operational programme which entails performing scientific tasks and testing equipment, similarly\nto what they are required to do on the ISS. The science program includes environmental and air circulation monitoring,\nmineralogy, microbiology, chemical composition of waters, and search for life forms adapted to the cavern environment. The CAVES 2012 Course will be explained and the first interesting scientific results will be presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".