Bibliographic record
Abstract
More than 230,000 known species representing 31 metazoan phyla populate the world's oceans. Perhaps another 1,000,000 or more species remain to be discovered. There is reason for concern that species extinctions may out-pace discovery, especially in diverse and endangered marine habitats such as coral reefs. DNA barcodes (i.e., short DNA sequences for species recognition and discrimination) are useful tools to accelerate species-level analysis of marine biodiversity and to facilitate conservation efforts. This review focuses on the usual barcode region for metazoans: a approximately 648 base-pair region of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Barcodes have also been used for population genetic and phylogeographic analysis, identification of prey in gut contents, detection of invasive species, forensics, and seafood safety. More controversially, barcodes have been used to delimit species boundaries, reveal cryptic species, and discover new species. Emerging frontiers are the use of barcodes for rapid and increasingly automated biodiversity assessment by high-throughput sequencing, including environmental barcoding and the use of barcodes to detect species for which formal identification or scientific naming may never be 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".