Bibliographic record
Abstract
When it comes to drinking water, where there’s lead, there’s usually iron. Researchers have established a correlation between the two metal contaminants but lack a clear understanding of how iron influences the release of toxic lead from pipes into water. And that relationship may be more complex than water experts anticipated, according to Graham Gagnon, director of the Centre for Water Resources Studies at Dalhousie University. Iron mineral nanoparticles may play a hitherto unconsidered role in helping lead leach into tap water, Gagnon said at the American Chemical Society national meeting in Philadelphia. Researchers who study drinking water currently consider metals in two forms, dissolved or particulate, the latter referring to metallic bits larger than about 450 nm, Gagnon explained in a session sponsored by the Division of Colloid & Surface Chemistry. Colloidal nanoparticles don’t fit squarely into either category. Yet these particles, which are roughly 10 nm in diameter,
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".