Exchanging reputation information between communities: a payment-function approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mercury is considered the most important heavy-metal pollutant, because of the likelihood of bioaccumulation and toxicity. Monitoring widespread ionic mercury (Hg(2+)) contamination requires high-throughput and cost-effective methods to screen large numbers of environmental samples. In this study, we developed a simple and sensitive analysis for Hg(2+) in environmental aqueous samples by combining a microfluidic immunoassay and solid-phase extraction (SPE). Using a microfluidic platform, an ultrasensitive Hg(2+) immunoassay, which yields results within only 10 min and with a lower detection limit (LOD) of 0.13 μg/L, was developed. To allow application of the developed immunoassay to actual environmental aqueous samples, we developed an ion-exchange resin (IER)-based SPE for selective Hg(2+) extraction from an ion mixture. When using optimized SPE conditions, followed by the microfluidic immunoassay, the LOD of the assay was 0.83 μg/L, which satisfied the guideline values for drinking water suggested by the United States Environmental Protection Agency (USEPA) (2 μg/L; total mercury), and the World Health Organisation (WHO) (6 μg/L; inorganic mercury). Actual water samples, including tap water, mineral water, and river water, which had been spiked with trace levels of Hg(2+), were well-analyzed by SPE, followed by microfluidic Hg(2+) immunoassay, and the results agreed with those obtained from reduction vaporizing-atomic adsorption spectroscopy.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it