Rapid DNA barcoding‐based fern and lycophyte inventories of protected areas—A pilot study to introduce a simple but effective protocol
Bibliographic record
Abstract
Abstract Recording inventories of species conserved in protected areas is a key step to evaluate the effectiveness of Kunming Montreal Global Biodiversity Framework (KM‐GBF) targets, such as the expansion of protected areas. The application of DNA barcoding facilitates the rapid production of enables to obtain rapid inventories with reduced reliance on taxon experts. These inventories aim not only to confirm existing records but also to minimize gaps in our knowledge of the distribution and taxonomy of species targeted for conservation through the implementation of protected areas. This pilot study introduces a simplified DNA barcoding pipeline as a reliable tool for recording fern and lycophyte species occurring in protected areas. The pipeline emphasizes limited and/or short training requirements, reducing the input required from taxon experts and maximizing shared benefits between conservationists and taxonomists. Despite using a single DNA barcoding region, 78% of the accessions were unambiguously identified to the species level. This applied approach not only confirmed previous records but also identified several previously overlooked species, either as newly recorded species conserved in the protected area or as species new to science. The pilot project effectively documented known species diversity and identified gaps in our taxonomic knowledge by discovering previously unknown and locally rare taxa. This rapid assessment enhances productive exchanges between conservation practitioners and taxon experts, with substantial benefits for both parties.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".