Environmental <scp>DNA</scp> (<scp>eDNA</scp>) Quantitative Polymerase Chain Reaction‐Based Assays for Surveying 125 Taxa of Importance to North America
Bibliographic record
Abstract
ABSTRACT Timely and accurate assessment of the presence of at‐risk or invasive species is critical for effective responses to climate change and human impacts. For example, at‐risk species are often difficult to find, while invasive species are often well established before their infiltration is detected using conventional surveying methods. However, all organisms release genetic material such as DNA into their surroundings, leaving traces of themselves that can be detected using environmental DNA (eDNA) methods. These approaches are powerful tools in the conservation toolbox, as they are transforming how risk assessments and the evaluation of mitigation and remediation effectiveness are done. Despite this, poorly performing tools hinder broad adoption of eDNA‐based detection methods, due in part to their associated high false negatives and false positives that can impair effective management decision‐making. iTrackDNA is a multi‐year, large‐scale applied research project that is addressing these concerns with researchers and end users from various sectors across North America. It is building end‐user capacity through innovative, accessible, socially responsible genomics‐based analytical eDNA tools for effective decision‐making by publishing 125 quantitative real‐time polymerase chain reaction (qPCR) primer/probe sets designed to detect key invertebrates, fish, amphibians, birds, reptiles, and mammals in coastal and inland ecosystems important to North America, with an emphasis on Canada. These 125 assays were designed to meet or exceed the new Canadian Standards Association (CSA) consensus‐based and multi‐stakeholder national standards for eDNA (CSA W214:21 and CSA W219:23). Herein, we describe how we applied eDNA assay design and validation approaches across a wide range of animal taxa to achieve compliance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".