Development and field validation of <scp>RPA‐CRISPR‐Cas</scp> environmental <scp>DNA</scp> assays for the detection of brown trout (<i>Salmo trutta</i>) and Arctic char (<i>Salvelinus alpinus</i>)
Bibliographic record
Abstract
Abstract Molecular methods are rapidly evolving to enable nucleic acid diagnostics outside a laboratory setting. Such techniques are primarily utilizing isothermal amplification such as Recombinase Polymerase Amplification (RPA) and Loop‐Mediated Isothermal Amplification (LAMP) but are yet to be fully explored for monitoring using environmental DNA (eDNA). We previously presented an RPA‐CRISPR‐Cas approach for detection of Atlantic salmon in Ireland and Canada and in this manuscript we present a further application of this technique for monitoring of brown trout and Arctic char in the Burrishoole Catchment, Co. Mayo, Ireland. In developing these assays, we offer an alternative approach to the PCR‐based assays previously published and have evolved a streamlined approach to single‐species monitoring using RPA‐CRISPR‐Cas, reducing the fluorescence acquisition time from 2 h to 30 min. This demonstrates the applicability of using RPA‐CRISPR‐Cas assays for eDNA‐based detection beyond Atlantic salmon with the added benefit of a faster assay time without compromising detection sensitivity.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".