Bibliographic record
Abstract
A nonlinear model for the x-ray linear attenuation coefficient μ is employed for dual energy x-ray analysis (DEXA). Nonlinear simultaneous equations formed by μ and energy dependent model parameters are solved for the electron density N(e) and fourth compositional ratio R(4) which has the same 'units' as the atomic number. Computed tomography data was acquired at 20-35 keV using bending magnet synchrotron radiation, a double crystal monochromator, a rotation stage and an area detector. Test objects contained liquid samples as mixtures of ethanol, water and salt solutions with known density and composition. Various noise sources are identified and give μ uncertainties of 1-2%. A fan beam geometry allowed the detection of forward scattered radiation with measured μ being 6% lower than expectations for a narrow beam. Energy dependent model parameters were obtained by solving linear simultaneous equations formed by μ and material parameters based upon N(e) and R(4). DEXA accuracy was studied as a function of photon energy and sample composition. Propagation of errors analysis identifies the importance of the fractional compositional cross-products whose difference at the two beam energies should exceed 0.1, requiring 10 keV or more separation. For a reasonable approximation for the adjustable model parameters, the mean difference between the DEXA solution and true values (ΔN(e), ΔR(4)) are (1.0%, 0.5%) for soft tissue and (1.5%, 0.8%) for bone like samples.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".