Hide and seek: molecular barcoding clarifies the distribution of two cryptic duckweed species across Alberta
Bibliographic record
Abstract
Regional and global biodiversity may be underestimated due to the presence of cryptic species: species that are morphologically similar, but genetically distinct. Here, we focus on two cryptic duckweed species, Lemna minor L. and Lemna turionifera Landolt, which have overlapping geographic ranges and are easily mistaken for one another. We developed species-specific primers based on DNA barcoding sequences to facilitate the rapid identification of these two monomorphic duckweeds, allowing us to investigate their presence and distribution in Alberta, Canada. While current reports indicate the presence of L. turionifera (and the morphologically distinct Lemna trisulca L.) in Alberta, our data indicate that L. minor is also present, predominantly in the southern part of the province. Thus, this paper (i) contributes to the accuracy and completeness of a regional flora, and (ii) provides useful and flexible tools for the rapid molecular identification of cryptic Lemna species, which are of wide interest in diverse fields such as biotechnology, toxicology, bioremediation, and ecology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".