Bibliographic record
Abstract
Evolving challenges require evolving responses. The use of illicit performance enhancing drugs by athletes permeates the reality and the perception of elite sports. New drugs with ergogenic or masking potential are quickly adopted, driven by a desire to win and the necessity of avoiding detection. To counter this trend, anti-doping authorities are continually refining existing assays and developing new testing strategies. In the post-genome era, genetic- and molecular-based tests are being evaluated as potential approaches to detect new and sophisticated forms of doping. Transcriptome analysis, in which a tissue's complement of mRNA transcripts is characterized, is one such method. The quantity and composition of a tissue's transcriptome is highly reflective of milieu and metabolic activity. There is much interest in transcriptional profiling in medical diagnostics and, as transcriptional information can be obtained from a variety of easily accessed tissues, similar approaches could be used in doping control. This article briefly reviews current understanding of the transcriptome, common methods of global analysis of gene expression and non-invasive sample sources. While the focus of this article is on anti-doping, the principles and methodology described could be applied to any research in which non-invasive, yet biologically informative sampling is desired.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".