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Record W4409348813 · doi:10.1063/4.0000404

Sharing our Excitement for Structural Science Through our Trainees

2025· article· en· W4409348813 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueStructural Dynamics · 2025
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceData science

Abstract

fetched live from OpenAlex

One of the most important means by which we can share our enthusiasm for structural science is through our trainees. Our students, both undergraduate and graduate, and postdoctoral researchers in our groups gain more than just technical skills through our interactions, they gain their own appreciation and excitement for science that they then can spread through their connections and contacts. We play an important role in garnering that excitement, fostering inquiry, and passing on that excitement to others. We often recount where our enthusiasm began, that one Professor or colleague whose excitement was infectious. In the Canadian context, Professor Michael James (1940-2023) was such a figure. Throughout his career, Michael’s excitement, and passion for the structural study of proteins, particularly proteolytic enzymes, fostered many who now continue that legacy and look to pass-on that excitement through their own interactions with trainees. This talk is a short remembrance of Michael, and others, who fostered that excitement in myself and others.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.298
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it