Deployment of a Prototype Plant GFP Imager at the Arthur Clarke Mars Greenhouse of the Haughton Mars Project
Bibliographic record
Abstract
The use of engineered plants as biosensors has made elegant strides in the past decades, providing keen insights into the health of plants in general and particularly in the nature and cellular location of stress responses. However, most of the analytical procedures involve laboratory examination of the biosensor plants. With the advent of the green fluorescence protein (GFP) as a biosensor molecule, it became at least theoretically possible for analyses of gene expression to occur telemetrically, with the gene expression information of the plant delivered to the investigator over large distances simply as properly processed fluorescence images. Spaceflight and other extraterrestrial environments provide unique challenges to plant life, challenges that often require changes at the gene expression level to accommodate adaptation and survival. Having previously deployed transgenic plant biosensors to evaluate responses to orbital spaceflight, we wished to develop the plants and especially the imaging devices required to conduct such experiments robotically, without operator intervention, within extraterrestrial environments. This requires the development of an autonomous and remotely operated plant GFP imaging system and concomitant development of the communications infrastructure to manage dataflow from the imaging device. Here we report the results of deploying a prototype GFP imaging system within the Arthur Clarke Mars Greenhouse (ACMG) an autonomously operated greenhouse located within the Haughton Mars Project in the Canadian High Arctic. Results both demonstrate the applicability of the fundamental GFP biosensor technology and highlight the difficulties in collecting and managing telemetric data from challenging deployment environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".