Bibliographic record
Abstract
Conventional DNA barcoding uses an approximately 650 bp DNA barcode of the mitochondrial gene COI for species identification in animal groups. Similar size fragments from chloroplast genes have been proposed as barcode markers for plants. While PCR amplification and sequencing of a 650 bp fragment is consistent in freshly collected and well-preserved specimens, it is difficult to obtain a full-length barcode in older museum specimens and samples which have been preserved in formalin or similar DNA-unfriendly preservatives. A comparable issue may prevent effective DNA-based authentication and testing in processed biological materials, such as food products, pharmaceuticals, and nutraceuticals. In these cases, shorter DNA sequences-mini-barcodes-have been robustly recovered and shown to be effective in identifying majority of specimens to a species level. Furthermore, short DNA regions can be utilized via high-throughput sequencing platforms providing an inexpensive and comprehensive means of large-scale species identification. These properties of mini-barcodes, coupled with the availability of standardized and universal primers make mini-barcodes a feasible option for DNA barcode analysis in museum samples and applied diagnostic and environmental biodiversity analysis.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.061 |
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".