Finding the pond through the weeds: <scp>eDNA</scp> reveals underestimated diversity of pondweeds
Bibliographic record
Abstract
Premise of the Study The detection of environmental DNA (eDNA) using high‐throughput sequencing has rapidly emerged as a method to detect organisms from environmental samples. However, eDNA studies of aquatic biomes have focused on surveillance of animal species with less emphasis on plants. Pondweeds are important bioindicators of freshwater ecosystems, although their diversity is underestimated due to difficulties in morphological identification and monitoring. Methods A protocol was developed to detect pondweeds in water samples using atpB‐rbcL and ITS2 markers. The water samples were collected from the Grand River within the rare Charitable Research Reserve, Ontario (RARE). Short fragments were amplified using primers targeting pondweeds, sequenced on an Ion Torrent Personal Genome Machine, and assigned to the taxonomy using a local DNA reference library and GenBank. Results We detected two species earlier documented at the experimental site during ecological surveys (Potamogeton crispus and Stuckenia pectinata) and three species new to the RARE checklist (P. foliosus, S. filiformis, and Zannichellia palustris). Discussion Our targeted approach to track the species composition of pondweeds in freshwater ecosystems revealed underestimation of their diversity. This result suggests that eDNA is an effective tool for monitoring plant diversity in aquatic habitats.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".