XAS at the Canadian Macromolecular Crystallography Facility
Bibliographic record
Abstract
Recent additions to the Canadian Macromolecular Crystallography Facility have expanded the capabilities of its bending magnet beamline. It is now possible to perform x-ray absorption spectroscopy (XAS) on crystals. A wide range of biologically relevant metals can be further studied, supplementing diffraction data. XAS can be used to determine if metalloproteins are photoreducing during diffraction data collection. The geometries of metal complexes can also be inferred with near-edge and EXAFS data, often more accurately than crystallography. CMCF-BM can be employed to do the abovementioned techniques on powder and solution samples that contain a metal of interest. One XAS-based technique that shows promise is single crystal plane polarized EXAFS. This technique combines crystallographic data with the findings from XAS to yield a high resolution three dimensional atomic model. More recently a number of the procedural steps required for the acquisition of XAS-based data have been automated in the MxDC software suite. These changes to data collection make it easier for users new to these disciplines to run the XAS-based experiments. By having the necessary equipment to do XAS at a protein crystallography facility, researchers who may not have had the opportunity delve into the field of XAS now can do so with minimal risk in terms of materials, funds and time.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.114 | 0.029 |
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".