Bibliographic record
Abstract
We chose Cytoskeleton for several key reasons. Firstly, the journal's scope aligned with our research focus on microtubule stability at mitotic spindle poles. Secondly, we appreciated Cytoskeleton's explicit interest in publishing a mix of experimental and theoretical work, spanning both in vitro and in vivo studies, as well as computational modeling. We found that these criteria corresponded well to the approaches we employed in our project. Finally, as a Wiley journal, Cytoskeleton has an open access agreement through Wiley-CRKN with Mount Saint Vincent University (Canada), which is hosting the Borgal lab. Making sure our work is open access aligns with our commitment to scientific transparency and spreading knowledge equitably. The work presented in this publication bridges two research labs: the James Wakefield lab (University of Exeter, UK) where I performed my bachelor's thesis and the Cell Proliferation Lab at Mount Saint Vincent University (Canada), where the work was completed by Principal Investigator Lori Borgal and her colleagues. The Wakefield lab uses the model organism Drosophila to study the mitotic spindle apparatus, from a microtubule and associated-protein perspective. Particularly, the lab focuses on characterizing the spindle's formation and robustness against stochastic environment perturbations. Building on these fundamental research questions, the Borgal lab uses both Drosophila and human cell culture to discover how the mitotic spindle forms in different stem cell types and how this impacts stem cell progeny proliferation and tissue homeostasis. While I've been lucky to interact with many outstanding scientists, the most profound influences on my career growth have been my direct mentors. Working closely with Isabelle Journdain during my very first lab internship, Lori Borgal during my bachelor's thesis, and Ana Stankovic throughout my Master's and PhD has been inspiring and formative. They not only provided training in project design, technical skills and critical thinking, but also exemplified how to navigate the complexities of a scientific career with integrity and resilience. Their mentorship extended beyond the bench, as they've all in turn offered advice on how to maintain a work-life balance and how to uphold rigor and passion for science even under pressure. Outside the lab, I spend much of my time creating ceramic art at my local pottery studio. I especially enjoy wheel throwing, where I shape a formless lump of clay into something unique and beautiful, purely using my hands. I actually see many similarities between my pottery hobby and my passion for research: both rely on creativity and patience, require highly specialised skills, and allow for continuous learning and self-improvement. But above all, I find both to be a lot of fun! The first thing I typically do when I wake up is to check the weather forecast on my phone, so that I can plan my morning schedule and my dog's training session before getting out of bed. I have an energetic 3-year-old dog that demands physical and mental exercise daily, and she can be temperamental about weather conditions. By checking the weather, I can adapt our training session and avoid doing trust-building exercises in the pouring rain.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.201 |
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".