Bibliographic record
Abstract
Computed tomography (CT) developed by Hounsfield (Hounsfield, 1973) extended the usefulness of radiography as a diagnostic technique. It enabled users to examine soft tissue in axial images, which previously had been limited mainly to hard tissue, e.g. bone and tooth. This was accomplished by the depiction of air space surrounding the soft tissue. Moreover, the development of high-speed scanning CT enabled three-dimensional observation of the anatomical structure of the human body. Slip-ring CT was developed by Toshiba in 1985 (Mori et. al., 1987) and by Siemens in 1987 (Kalender et. al., 1997), and Helical CT was in the practical application by the both companies in 1990 (Katakura, 1989) (Kalender et. al., 1990) (Kageyama et. al., 1992). Furthermore, CT imaging in biomedical science has transcended its use as a diagnostic tool only. For example, three-dimensional reconstruction through rapid-prototyping techniques has been accomplished through application of CT imaging for novel use. In this chapter, a standardized method for airway extraction from CT of the head and neck region is described. This process applies further to simulation of speech and swallowing, and for diagnosis of sleep apnea syndrome (SAS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".