Development of Standard Operating Procedures (SOPs) for the Use of the Model Plant Arabidopsis thaliana in Molecular Biology Research
Bibliographic record
Abstract
Arabidopsis thaliana is a model plant used to study molecular processes that control plant growth and development. It is popularly studied due to its easy maintenance, short generation time, fully sequenced genome and high offspring yield. However, plants can be sensitive to environmental changes and require proper handling. Therefore, standard operating procedures (SOPs) for maintaining Arabidopsis must be optimised for the location that they are being maintained. This project was conducted to develop custom SOPs for MacEwan University for future undergraduate molecular biology research with Arabidopsis thaliana. The SOPs define optimal plant growing conditions and provide step-by-step instruction for seed harvest and storage. In addition, techniques necessary for genetic engineering of Arabidopsis were also tested. SOPs regarding plasmid DNA cloning, purification, quantification and remodelling using Escherichia coli were created. Seeds were also harvested and sterilized throughout the course of the study, providing MacEwan University with a dedicated Arabidopsis seed bank. Researchers at MacEwan University interested in using Arabidopsis thaliana now have immediate access to viable seeds and the information necessary for ensuring plant health and proper plasmid DNA preparation. Faculty Mentor: Melissa Hills Department: Biological Science
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.027 |
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".