The Robotic Molecular Biologist -Automated Processing of Astrobiological Samples for Planetary Missions
Bibliographic record
Abstract
Nanopore based DNA sequencing is becoming more prevalent in life-detection platforms, and the search for life in our solar system is a major focus of planetary exploration.One of the key challenges is the development of a suite of life detection instruments with lower mass and energy requirements.The aim of this research is the development of a low-mass and robust life detection platform that has the potential to be integrated into future astrobiology space missions and to perform numerical analysis to optimize the platform.To achieve this, a subset of instruments that could be deployed on a Mars rover are examined and a novel stand-alone life detection platform is designed that is equipped with peristaltic pumps, solenoid valves, microfluidic chip, Microbial Activity MicroAssay (µ-MAMA) and an Automated Nucleic Acid Extraction System (A-NECS).Further, a control algorithm is developed to perform an end-to-end analysis of DNA/RNA extraction and microbial activity detection on the environmental sample collected.A mathematical model for predicting the flow patterns in microfluidics is presented and a CFD analysis of the lysing chamber is conducted to understand turbulent properties, volume density distributions of the dissipated energy and the agitation rate effects.MRF method is used for calculations and two different zones of the lysing chamber are investigated.It is found to improve the accuracy in the prediction of the results without increasing the overall cost, mass, energy consumption of the instrument suite.Further, the results obtained in the thesis indicate that the automated life-detection platform performed significantly well and microbial activity was detected in the sample tested.iii List of Tables 3.1 Micropump parameters and their specifications. . . . . . . . . . . .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".