The Study of Drosophila Mate Preference as a Research Assistant in A. Moehring’s Lab
Bibliographic record
Abstract
My name is Joyce Liu and I am going into my 4th year of the Honours Double Major in Biology and Physiology program. I decided to pursue a double major because I was intrigued by many different aspects of the biological sciences. Specifically, I was very interested in translational research; how findings from basic science can be applied and used to further understand human diseases and behaviour. One area that I wished to learn more about was behavioural genetics. Through the Work Study program at Western, I was able to complement my studies with my interests by participating in research with Ryan Calhoun, a PhD candidate at Dr. Amanda Moehring’s lab. I found this experience to be very rewarding because I witnessed first-hand the amount of dedication, hard work, and commitment that goes into research. It has really inspired me to push myself further in hopes of making a difference in the scientific community. In this article, I will further elaborate on the role that I played as a research assistant, how this experience changed my perception of research, and why it changed my future career goals.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".