Bibliographic record
Abstract
This recent meeting, held on the campus of the University of British Columbia, attracted 1200 delegates and a vast array of talks, but was notable for a remarkable showing of talks and posters on DNA barcoding in plants, spread through many sessions. The Canadian Centre for DNA Barcoding defines barcoding as 'species identification and discovery through the analysis of short, standardized gene regions known as DNA barcodes'. This approach is somewhat controversial in animals (Rubinoff et al., 2006), although it has been shown to be useful and reliable in many metazoan taxa (Meyer & Paulay 2005; Hajibabaei et al., 2007), in which the mitochondrial cytochrome oxidase I (COI) gene is used. However, in land plants, COI evolves far too slowly to be useful, and there is no obvious single universal alternative (Fazekas et al., 2008).Genes that work well in one taxon may perform poorly in other taxa. Additionally, some perfectly good plant species,reproductively isolated and morphologically and ecologically distinct, are too young to show much sequence divergence at most loci. Nevertheless, as we saw at this conference, progress has been made towards identifying genes that serve many of the functions of DNA barcodes, at least in some plant taxa.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".