Queen's Genetically Engineered Machine Team (QGEM): Nemoremediation
Bibliographic record
Abstract
Synthetic biology is a rapidly growing field that tries to simplify genes into “biobricks” and use these to push the limits of what is possible in genetic engineering. The Queen's Genetically Engineered Machine Team competes annually at the International Genetically Engineered Machine Competition, one of the largest undergraduate research conferences on the planet. Last year’s project focused on modifying the nematode worm, C. Elegans to chemotax, or seek out and degrade pollutants, such as naphtalene. We have produced genetic constructs with protein receptors from M. musculus, R. norvegicus, and H. sapiens intended to enhance the worm's ability to chemotax towards naphthalene and other pollutants. We also worked on a field bioassay based on fluorescent proteins that will indicate the presence of naphthalene in a soil sample. The goal is to have a population of green fluorescent worms chemotaxing toward and a population of red fluorescent worms chemotaxing away from the napthalene in the soil sample. Finally, we have added the P. putida gene, nahD, to the biobrick registry, which encodes a degradative enzyme as part of a naphthalene catabolic pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".