Bibliographic record
Abstract
Tim Veenstra is the Director of the Laboratory of Proteomics and Analytical Technologies at the National Cancer Institute at Frederick, MD, USA. Veenstra acquired his PhD in biochemistry from the University of Windsor, Canada, in 1994 under the guidance of Lana Lee. He then moved to the laboratory of Rajiv Kumar at the Mayo Clinic in Rochester, MN, USA, where he completed a postdoctoral fellowship in molecular biology. He has been at his current position for 7 years. The focus of his research deals primarily with the discovery of novel biomarkers for diseases such as cancer. To accomplish this goal, his laboratory has developed methods to analyze the proteomes and metabolomes of thin sections obtained from both fresh-frozen and formalin-fixed paraffin-embedded tissues. His laboratory is also interested in developing and applying methods to more effectively characterize the proteomes and metabolomes of various biofluids for the discovery of both diagnostic and therapeutic biomarkers.Katrin Marcus studied Biochemistry at the Ruhr-University Bochum, Germany. After finishing her diploma thesis, she elaborated her PhD at the Proteinstrukturlabor of Professor HE Meyer in Bochum, Germany, analyzing the phosphoproteome of human thrombin-stimulated platelets. Since August 2002, she has been group leader at the Medizinisches Proteom-Center, supervising several projects such as the ‘Human Brain Proteome Project’ and ‘Clinical Neuroproteomics of Neurodegenerative Diseases’. In August 2003, she was appointed as an Assistant Professor for proteomics at the Medical Faculty of the Ruhr-University. In December 2007, she became full Professor and now leads the Department of Functional Proteomics. Her scientific work is focused on the discovery of biomarkers for Alzheimer’s and Parkinson’s disease, and the analysis of integral membrane proteins of human hepatocytes that play a pivotal role in all phases of xenobiotics metabolism.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".