<i>BI&T</i> Editorial Board Selects Best Paper Awards of 2005
Bibliographic record
Abstract
The journal's Editorial Board has voted on the two best papers published in the 2005 issues of BI&T. The authors will be recognized during the AAMI Conference & Expo to be held June 24-26 in Washington, D.C.The first winning paper, selected as the best “Management & Technology” article, is titled “Gait Analysis” and was published in the January/February 2005 issue.Co-authored by Victoria L. Chester, Edmund N. Biden, and Maureen Tingley, the article examined how gait analysis, or the study of locomotion, has changed over the last few decades. Advances in computer technology and data analysis techniques have contributed greatly to the progress of this field. The paper discussed the experimental and analytical techniques used for performing clinical gait analyses at the University of New Brunswick in Canada.The second winning paper, “Development of High-Sensitivity Near Infrared Fluorescence Imaging Device for Early Cancer Detection,” was awarded the best “Instrumentation Research.” The manuscript appeared in the January/February 2005 issue.The paper was co-written by Yu Chen, Xavier Intes, and Britton Chance. The team from the University of Pennsylvania developed a high-sensitivity near-infrared (NIR) optical imaging system for noninvasive cancer detection based on the molecular-labeled fluorescent contrast agents. The authors discuss how the instrument has the potential for tumor diagnosis and imaging, and how it could help guide the clinical fine-needle biopsy.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.027 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.161 | 0.143 |
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".