Bibliographic record
Abstract
One of the most important means by which we can share our enthusiasm for structural science is our mentorship of trainees. Our trainees at all levels gain more than just technical skills from the time we spend with them; they develop their own appreciation and excitement for structural science that they then can spread through their connections and contacts. We play an important role, through our mentorship, in encouraging that excitement, fostering inquiry, and passing on that excitement to others. We often recount where our enthusiasm began, with one or more professors, mentors and/or colleagues whose excitement was infectious and helped us along our own professional journey and development of our own mentorship philosophies. In the current article, I outline how several mentors, including Professors Michael James, Louis Delbaere, Wilson Quail, and others, instilled that excitement for structural science in me and provided examples from which I have developed my perspective on mentorship and how we can pay it forward, supporting and instilling excitement in our trainees.
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 | 0.038 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.021 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".