Bibliographic record
Abstract
1. Characterising metal ion interactions with biological molecules - the spectroscopy of metallothionein Martin J. Stillman and Anthony Presta 2. Biochemical pathways in cadmium toxicity M.H. Bhattacharyya, A.K. Wilson, S.S. Rajan and M. Jonah 3. Arsenic Eric Wildfang, Shiela M. Healy and Vasken Aposhian 4. Chromium and nickel Max Costa 5. Transport of soft metals in prokaryotes Christopher Rensing and Barry P. Rosen 6. Metal transport and metabolism Michael Didonato and Bibudhendra Sarkar 7. The role of ion channels in the transport of metals into excitable and non-excitable cells Timothy J. Shafer 8. Responses of the respiratory tract to cadmium Beth A. Hart 9. Mercury: molecular interactions and mimicry in the kidney Rudolfs K. Zalups 10. Transport of metals in the nervous system Michael Aschner and Laura E. Kerper 11. Transport of metals in the gastrointestinal system and kidneys Gary L. Diamond 12. Molecular mechanisms of hepatic metal transport Nazzareno Ballatori 13. Role of glutathione in the metabolism, transport and toxicity of metals Lawrence H. Lash 14. Role of metallothionein in the metabolism, transport and toxicity of metals Michael P. Waalkes and Raul Perez-Olle 15. Metal ions and the cytoskeleton Douglas M. templeton 16. The role of metals in oxidative damage and redox cell signaling derangement Kazimierz S. Kasprzak 17. Copper ion regulation of gene expressions in yeast Laran T. Jensen and Dennis R. Winge 18. Toxic and essential metals in cellular response to signals James Koropatnick and Rudolfs K. Zalups Aschner, Wake Forest University School of Medicine, USA, Nazzareno Ballatori, University of Rochester School Of Medicine, USA, M.H. Bhattacharyya, Centre for Mechanistic Biology, USA, Gregory S. Buzard, Frederick Cancer Research and Development Centre, USA, Max Costa, New York University Medical Centre, USA, Gary L. Diamond, Syracuse Research Corporation, USA, Michael DiDonato, The Hospital for Sick Children, Toronto, Canada, Beth A. Hart, University of Vermont College of Medicine, USA, Sheila M. Healy, University of Arizona, USA, Laran T. Jensen, University of Utah Health Sciences Centre, USA, Margaret M. Jonah, Dominican University, USA, Kazimierz S. Kasprzak, Frederick Cancer Research and Development Centre, USA, Laura E. Kerper, University of Rochester School Of Medicine and Dentistry, USA, James Koropatnick, London Regional Cancer Centre, Ontario, Canada, Lawrence H. Lash, Wayne State University School of Medicine, USA, Raul Perez-Olle, National Cancer Institute at NIEHS, USA, Anthony Presta, University of Western Ontario, Canada, S.S. Rajan, Centre for Mechanistic Biology, USA, Christopher Rensing, Wayne State University School of Medicine, USA, Barry P. Rosen, Wayne State University School of Medicine, USA, Bibudhendra Sarkar, University of Toronto, Canada, Timothy J. Shafer, US Environmental Protection Agency, USA Martin J. Stillman, University of Western Ontario, Canada, Douglas M. Templeton, University of Toronto, Canada, Michael P. Waalkes, National Cancer Institute at NIEHS, USA, Eric Wildfang, University of Arizona, USA, A.K. Wilson, Benedictine University, USA, Dennis R. Winge, University of Utah Health Sciences Centre, USA, Rudolfs K. Zalups, Mercer University School of Medicine, USA.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".