Applications of computed tomography to fossil conservation and education
Bibliographic record
Abstract
Computed tomography (CT) has been used for decades for paleontological research and fossil preparations. However, the benefits of CT scanning regarding conservation, exhibits, and education are rarely discussed. CT and rapid prototyping, although still prohibitively expensive on a large scale, are becoming cheaper and can provide another tool available to museums and educators teaching natural history.Resumen. Tomografía computarizada (TC) ha sido usada durante décadas en investigaciones paleontológicas y geológicas. A pesar de esto, la aplicación de esta tecnología, en conjunto con impresoras tridimensionales y la rápida producción de prototipos, apenas se utilizan para suplementar educación, conservación, y exhibición dentro de estos campos. El desarrollo de esta tecnología, la reducción de costos y la aumentada precisión de estos productos, los hace más accesibles para instituciones. Aunque predomina su uso en rubros investigativos también se puede extender a profesionales asociados con la historia natural. Este papel brevemente menciona los principios de TC y su rol investigativo, pero enfoca en desarrollar el uso de CT en conjunto con la rápida producción de prototipos para conservación de material geológico y el uso de tal como material educacional.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".