Reference library for targeted SNP-based identification of Cibotium barometz using Next Generation Sequencing
Bibliographic record
Abstract
Cibotium is a genus of tropical tree ferns with a highly debated species count. One species in particular, Cibotium barometz, also known as the golden chicken fern or woolly fern, attracts much attention and is listed in CITES Appendix II. The species is native to China and to the western part of the Malay Peninsula. Traditional Chinese medicine values its anti-inflammatory, anti-rheumatic, tonic, styptic, antibacterial, antioxidant and anti-osteoporotic properties. There are no reports of C. barometz being cultivated in China thus all material is wild-sourced. Due to lack of export regulations, over 500 tons of dry rhizomes of C. barometz were exported from China in 1993 – 1997. In 2001, the annual quota for export from China was limited to 130 tons. High demand and CITES export permit requirements drive frequent mislabeling and false custom declaration attempts. Lack of clear diagnostic traits requires molecular-based tools for identification of dried C. barometz rhizomes. We used partial sequence data in GenBank to screen five regions: rps4 (ribosomal protein S4), atpA (ATP synthase alpha chain), intergeneric spacers trnG-trnR, rps4-trnS, and rbcL-atpB. We designed seven primer pairs targeting species-specific SNPs distinguishing C. barometz from closely related C. cumingii and other congeners. Reference library for these five markers was generated from decades-old herbarium material obtained from the UBC herbarium for eight species of Cibotium. The combination of five DNA markers distinguishes C. barometz from congeneric species, including its sister taxon C. cumingii. The resulting reference library linked to herbarium vouchers is applicable for regulatory purposes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".