Molecular Embodiments and the Body-work of Modeling in Protein Crystallography
Bibliographic record
Abstract
Protein molecules, those objects of increasing interest and investment in post-genomics research, are complex, three-dimensional structures made up of thousands of atoms. Protein crystallographers build atomic-resolution models of proteins using the techniques of X-ray diffraction. This ethnographic study of protein crystallography shows that becoming an expert crystallographer, and so making sense of such intricate objects, requires researchers to draw on their bodies as a resource to learn about, work with, and communicate precise molecular configurations. Contemporary crystallographic modeling relies intensively on interactive computer graphics technology, and requires active and prolonged handling and manipulation of the model onscreen throughout the often arduous process of model-building. This paper builds on both ethnographic observations of contemporary protein crystallographers and historical accounts of early molecular modeling techniques to examine the body-work of crystallographic modeling, in particular the corporeal practices through which modelers learn the intricate structures of protein molecules. Ethnographic observations suggest that, in the process of building and manipulating protein models, crystallographers also sculpt embodied models alongside the digital renderings they craft onscreen. Crystallographic modeling at the computer interface is thus not only a means of producing representations of proteins; it is also a means of training novice crystallographers' bodies and imaginations. Protein crystallographers' molecular embodiments thus offer a site for posing a new range of questions for studies of the visual cultures and knowledge practices in the computer-mediated life sciences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | MetaresearchScience and technology studies Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".