UK Space Agency “Mars Utah Rover Field Investigation 2016” (MURFI 2016): overview of mission, aims and progress
Bibliographic record
Abstract
The Mars Utah Rover Field Investigation “MURFI 2016” is a Mars Rover field analogue mission run by the UK Space Agency (UKSA) in collaboration with the Canadian Space Agency (CSA). MURFI 2016 took place between 22nd October and 13th November 2016 and consisted of a field team including an instrumented Rover platform, at the field site near Hanksville (Utah, USA), and an ‘Operations Team’ based in the Mission Control Centre (MOC) at the Harwell Campus near Oxford in the UK. The field site was chosen based on the collaboration with the CSA and its Mars-like local geology. It was used by the CSA in 2015 for Mars Rover trials, and in 2016, several teams used the site, each with their own designated working areas. \n The two main aims of MURFI 2016 were (i) to develop logistical and leadership experience in running field trials within the UKSA, and (ii) to provide members of the Mars Science community with Rover Operations experience, and hence to build expertise that could be used in the 2020 ExoMars Rover mission, or other future Rover missions. Because MURFI 2016 was the first solely UKSA-led Rover analogue trial, the most important objective was to learn how to best implement Rover trials in general. This included aspects of planning, logistics, field safety, MOC setup and support, communications, person management and science team development. Some aspects were based on past experience from previous trials but the focus was on ‘learning through experience’ - especially in terms of the Operations Team, who each took on a variety of roles during the mission.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".