Bibliographic record
Abstract
In recent years, a great tendency toward using diagnostic tests which are based on DNA has expanded.Thus, DNA-recognition biosensors have been created which can facilitate DNA identification.These DNA-oriented identification systems work based on hybridization of a target DNA with its complementary probe that can be performed in solution or on a solid surface.In this study, Zabol mildew melons were used as the model plant.After identify a specific sequence was of the species Cucumis melo L. Using NCBI and BLAST sites using the probe designed for specific sequencing, Identification of plant was performed using a probe attached to gold nanoparticles to observe color variation of gold nanoparticles in presence of the target molecules.In addition, the hybridization of the probes with target molecules was evaluated at a wavelength of 400 to 700 nm, so that maximum change was observed in the wavelength range of 550 to 650 nm.The results of this study showed that using detectors attached to gold nanoparticles is a more specific and rapid way to detect than the biochemical and molecular techniques .It can also be achieved spending lower costs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.937 | 0.948 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".