Making molecular biology fun: Fish fraud in Ontario
Bibliographic record
Abstract
Connecting the theory and practice in labs in a molecular biology course has been a challenge. Molecular cloning experiments incorporate theory learned in the course in one single experiment, but these types of experiments are long (>8 weeks to see results in our current lab and lecture format), are often unsuccessful by lab groups despite best practices, and may not be applicable to the future lives of graduates of the Chemical Laboratory Technology program. This course is taught by contract faculty, so spending a large amount of time redesigning course content is often not possible. The goal of this exercise was to find a pre-made, short (2 lab sessions maximum) experiment that would incorporate multiple topics learned in the course (DNA extraction, PCR, primer design, gel electrophoresis, PCR cleaning protocols, DNA sequencing, and DNA barcodes) and solidify those theoretical concepts in a practical setting using a protocol relevant to genetic-based molecular investigations in the present day. We used a Fish DNA Barcode kit to examine the frequency of fish mislabeling in restaurants and grocery stores in Southwestern Ontario (a protocol used by the CIFA in Canada and the FDA in the USA for monitoring food fraud). What we found was astounding: few of the samples were labeled correctly, and some were wildly mislabeled. The students were able to complete the experiment with a high success rate, and the experiment performed brings “real-world” experience into the lab environment. Participants in this session will get a detailed walk-through of the experiment, how it relates to course, program, and vocational learning outcomes of the CLT program using a non-traditional laboratory experiment.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.041 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".