Bibliographic record
Abstract
Small molecules play a significant role in a variety of applications. Current detection methods present limitations leaving an unmet need for alternative methods of detection. Selected through an in vitro process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), aptamers are single stranded oligonucleotides that can bind to specific target molecules with high selectivity and affinity. Aptamers can be used as the molecular recognition agent of various biosensors. Based on a non-covalent interaction, AuNPs could potentially serve as a novel platform for small molecule SELEX. Selecting for aptamers in a manner that will mimic established AuNP biosensor conditions provides a number of advantages. As a first step towards establishing a AuNP SELEX platform, we evaluated SELEX partitioning. Having uncovered several challenges, we next synthesized, optimized, and characterized Fe3O4-AuNPs. We demonstrated that Fe3O4-AuNPs could function as a novel method to study ssDNA aptamer-AuNP non-specific interactions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".