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
Proteins are essential components of organisms and are involved in a wide range of biological functions. There are increasing demands for ultra-sensitive protein detection, because many important protein biomarkers are present at ultra-low levels, especially during the early stages of disease. Measuring proteins at low levels is also crucial for investigations of the protein synthesis and functions in biological systems. In this review, we summarize the recent developments of novel technology enabling ultrasensitive protein detection. We focus on two groups of techniques that involve either polymerase amplification of affinity DNA probes or signal amplification by the use of nano-/micro-materials. The polymerase-based amplification of affinity DNA probes indirectly improves the sensitivity of protein detection by increasing the number of detection molecules. The use of nano-/micro-materials conjugated to affinity probes enhances the measurement signals by using the unique electrical, optical, and catalytic properties of these novel materials. This review describes the basic principles, performances, applications, merits, and limitations of these techniques.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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