Is a Large-Scale DNA-Based Inventory of Ancient Life Possible?
Bibliographic record
Abstract
A complete DNA-based inventory of the Earth's present biota using large-scale high-throughput DNA sequencing of signature region(s) (DNA barcoding) is an ambitious proposal rivaling the Human Genome Project. We examine whether this approach will also enable us to assess the past diversity of the earth's biota. To test this, we sequenced the 5' terminus of the mitochondrial cytochrome c oxidase I (COI) gene of individuals belonging to a group of extinct ratite birds, the moa of New Zealand. Moa comprised a large number of taxa that radiated in isolation on this oceanic landmass. Using a phylogenetic approach based on a large data set including protein coding and 12S DNA sequences as well as morphology, we now have precise information about the number of moa species that once existed. We show that each of the moa species detected using this extensive data set has a unique COI barcode(s) and that they all show low levels of within-species COI variation. Consequently, we conclude that COI sequences accurately identify the species discovered using the larger data set. Hence, more generally, this study suggests that DNA barcoding might also help us detect other extinct animal species and that a large-scale inventory of ancient life is possible.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".