Scaled-Up Paper Dipsticks for Nucleic Acid Extraction from Soil Samples
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
Nucleic acid extraction from soil samples holds paramount importance in various scientific domains, particularly in environmental microbiology, molecular ecology, and agricultural sciences. This process serves as a foundational step for numerous downstream applications, enabling a deeper understanding of soil microbial communities and their functions. Paper-based rapid nucleic acid extraction is the most cost-effective and easily accessible method available for nucleic acid extraction. In contrast to previous attempts at developing paper-based dipsticks for nucleic acid extraction, which could only analyze samples of volume < 10 μL, we report a method that enables the extraction of nucleic acids from samples 50 times larger in volume (50–650 μL). Our new design involves the use of paper-based dipsticks with corrugated edges and a pointed tip, which can be further joined together at the handle to create stacked dipsticks, thereby increasing the surface area of the dipstick in contact with the sample, and the volume of the sample from which nucleic acid can be extracted. The extracted DNA has later been quantified using a benchtop UV–vis spectroscopy-based DNA quantification device to calculate the extraction efficiency (%) of the samples under study. The application of our paper-based nucleic acid extraction dipstick has been demonstrated by conducting controlled experiments to extract nucleic acid from garden soil samples. The highest extraction yield (%) obtained was found to be approximately 52% for a soil sample spiked with DNA with a concentration of 1000 nM using a 10-stack dipstick.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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