Bibliographic record
Abstract
Fungi are ubiquitous in both natural and human-made environments. They play important roles in the health of plants, animals, and humans, and in broad ecosystem functions. Thus, having an efficient species-level identification system could significantly enhance our ability to treat fungal diseases and to monitor the spatial and temporal patterns of fungal distributions and migrations. DNA barcoding is a potent approach for rapid identification of fungal specimens, generating novel species hypothesis, and guiding biodiversity and ecological studies. In this mini-review, I briefly summarize (i) the history of DNA sequence-based fungal identification; (ii) the emergence of the ITS region as the consensus primary fungal barcode; (iii) the use of the ITS barcodes to address a variety of issues on fungal diversity from local to global scales, including generating a large number of species hypothesis; and (iv) the problems with the ITS barcode region and the approaches to overcome these problems. Similar to DNA barcoding research on plants and animals, significant progress has been achieved over the last few years in terms of both the questions being addressed and the foundations being laid for future research endeavors. However, significant challenges remain. I suggest three broad areas of research to enhance the usefulness of fungal DNA barcoding to meet the current and future challenges: (i) develop a common set of primers and technologies that allow the amplification and sequencing of all fungi at both the primary and secondary barcode loci; (ii) compile a centralized reference database that includes all recognized fungal species as well as species hypothesis, and allows regular updates from the research community; and (iii) establish a consensus set of new species recognition criteria based on barcode DNA sequences that can be applied across the fungal kingdom.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.027 |
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".