Bibliographic record
Abstract
Professor Jörg Feldmann was educated in Germany. After 25 years in Canada and Scotland working on the development of novel elemental speciation methods for unraveling biological and environmental pathways of trace elements, he moved to Austria. At the University of Graz, he became Head of the Analytical Chemistry Department and was recently appointed Head of the Institute for Chemistry. Currently, he is focusing mainly on the development of new platforms for fluorine and per- and polyfluoroalkyl substance (PFAS) analysis and is additionally investigating arsenic and mercury transformations and bioaccumulation pathways in the marine environment and in rice cultivation. Professor Feldmann has published more than 350 papers (h-index of 76), given over 180 invited lectures, and educated more than 50 PhD students. He has also received many awards, including the 2015 European Plasma Spectrochemistry Award, the 2016 RSC Interdisciplinary Award and Medal, the 2020 Award for Industrial Engagement, and the 2023 Award for Excellence in Teaching, and was elected a Fellow of the Royal Society of Edinburgh in 2018.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.063 | 0.025 |
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".