Cold Code: the global initiative to<scp>DNA</scp>barcode amphibians and nonavian reptiles
Bibliographic record
Abstract
Abstract DNA barcoding facilitates the identification of species and the estimation of biodiversity by using nucleotide sequences, usually from the mitochondrial genome. Most studies accomplish this task by using the gene encoding cytochrome oxidase subunit I (COI; EntrezCOX1). Within this barcoding framework, many taxonomic initiatives exist, such as those specializing in fishes, birds, mammals, and fungi. Other efforts center on regions, such as the Arctic, or on other topics, such as health.DNAbarcoding initiatives exist for all groups of vertebrates except for amphibians and nonavian reptiles. We announce the formation of Cold Code, the international initiative toDNAbarcode all species of these ‘cold‐blooded’ vertebrates. The project has a Steering Committee, Coordinators, and a home page. To facilitate Cold Code, the Kunming Institute of Zoology, Chinese Academy of Sciences will sequenceCOIfor the first 10 specimens of a species at no cost to the steward of the tissues.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.035 |
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".