Barcode 100K Specimens: In a Single Nanopore Run
Bibliographic record
Abstract
It is a global priority to better manage the biosphere, but action must be informed by comprehensive data on the abundance and distribution of species. The acquisition of such information is currently constrained by high costs. DNA barcoding can speed the registration of unknown animal species, the most diverse kingdom of eukaryotes, as the BIN system automates their recognition. However, inexpensive sequencing protocols are critical as the census of all animal species is likely to require the analysis of a billion or more specimens. Barcoding involves DNA extraction followed by PCR and sequencing with the last step dominating costs until 2017. By enabling the sequencing of highly multiplexed samples, the Sequel platforms from Pacific BioSciences slashed costs by 90%, but these instruments are only deployed in core facilities because of their expense. Sequencers from Oxford Nanopore Technologies provide an escape from high capital and service costs, but their low sequence fidelity has, until recently, constrained adoption. However, the improved performance of its latest flow cells (R10.4.1) erases this barrier. This study demonstrates that a MinION flow cell can characterise an amplicon pool derived from 100,000 specimens while a Flongle flow cell can process one derived from several thousand. At $0.01 per specimen, DNA sequencing is now the least expensive step in the barcode workflow.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".