Bibliographic record
Abstract
Biological Effects: Introduction and Early in vitro and in vivo Studies, D. McClain, PhD and A.C. Miller, PhD Characteristics, Biokinetics And Biological Effects Of Used In Weapons And The French Nuclear Industry, V. Chazel, PhD, P. Houpert, PhD. F. Paquet, PhD Carcinogenesis of Uranium: Studies in Animals, F.F. Hahn, DVM, PhD Neurotoxicology of in Adult and Developing Rodents, W. Briner, PhD Colorimetric Determination of Uranium, J.F. Kalinich, PhD and D.E. McClain, PhD Chemical And Histological Assessment Of In Tissues And Biological, T.I. Todorov, J.W. Ejnik, F.G. Mullick, and J.A. Centeno DU Exposure and Surveillance of Related Health Effects in U.S. Soldiers, K.S. Squibb, PhD and M.A. McDiarmid, MD, MPH Health Hazards Of Munitions: Estimates Of Exposures And Risks In The Gulf War, The Balkans And Iraq, B.G. Spratt, PhD Canadian Forces Testing Program, E.A. Ough, PhD Biokinetics of Embedded DU, R. Leggett, PhD Application of ICRP Biokinetic Models To Uranium, M.R Bailey and A.W. Phipps The Depleted Uranium And Radiological Hazard During Operations In Kosovo, P. Gerasimo, PhD, P. Laroche, and G. Romet, MD United Nations Environment Programme (UNEP): Results based on the three DU Assessments in the Balkans and the joint IAEA/UNEP Mission to Kuwait, M. Burger, PhD and H. Slotte, PhD
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".