Workshop on body composition in basic and clinical research and the emerging technologies
Bibliographic record
Abstract
A special one-day workshop was held to review the status, the need for, and the future role of BNL in the Body Composition Analysis Program (BCAP). Two speakers succinctly outlined the status and future new developments using gamma nuclear resonance technology as it applies to BCAP. Seven speakers from three institutions outlined the continued need for BCAP and presented new clinical applications of BCAP in theirs respective fields of expertise. Extensive increase in the use of surrogate instrumentation, e.g., DXA and BIA, in BCAP was recognized as a significant contributing factor to the growth in BCAP. The growing role of MRI in BCAP was also emphasized. In light of these developments BCAP at BNL, with its specialized In Vivo Neutron Activation (IVNA) facilities, was recognized as a unique user oriented resource that may serve the community hospitals in the area. Three regional large institutions expressed their desire to use these facilities. In addition, IVNA provides direct measure of the human compartments in vivo, thus providing a gold standard for the surrogate methodologies that are in use or to be developed. It was strongly felt that there is a need for a calibration center with a national stature for the different methodologies for in vivo measurements, a role that befits very well a national laboratory. This offers an exquisite justification for DOE to support this orphan technology and to develop BCAP at BNL to, 1, provide a user oriented regional resource, 2, provide a national reference laboratory, and 3, develop new advanced technologies for BCAP.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".