Bone Microstructure Revealed by Combined Sub-micron Resolution Diffraction and Fluorescence Tomography
Bibliographic record
Abstract
Imaging of samples on different length scales has an increasing impact in the use of X-rays and in an increasing amount of disciplines.Besides the already for some time established Computer-Tomography and its newest variant X-ray fluorescence tomography, the growing use of imaging techniques is due to the experimental access of the phase in various methods.This opened the door further to a spatial description of material properties.The investigation of structure-function-relationships would not be imaginable without imaging techniques.This development is accompanied with new challenges in its methodological use and image interpretation by the various disciplines and its production of a new quality and quantity of data.Common to all different imaging techniques is that the computer has become an essential component, if not the most important one, of the experiments.Evaluation of raw data and their visualization have to be seen as integral component of the experiments.Quantity and quality of measurements are nowadays essentially determined by computer operation.The aspect of quantity, so the production of enormous amounts of data, is actually a widely discussed challenge at large scale facilities requiring new concepts of data storage and data representation on site.The "Big Data" subject appears in a specific form.The other aspect, the one of quality, is fundamentally determined by computer programs.For example, defining spatial resolution has to distinguish between instrumentally achieved, spatial resolution and the one produced by reconstruction techniques.Shortly, the standardization and validation of the various imaging techniques is necessary.Otherwise the door to pure imagination will be opened widely.The fact that images in science are highly artificial and at the same time, as visual experience, intuitively convincing renders scientific handling of images into a challenge.Especially the aspects of validation and standardization are important, and can only be achieved by a thorough understanding of the methods.As in former times in science validation and standardization of emerging methods are important milestones, which we should go for analytical imaging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".