The riverscape on a chip: high-throughput qPCR enables basin-wide fishery assessments
Bibliographic record
Abstract
To achieve reliable detection of environmental DNA (eDNA) from multiple species, a method which combines the specificity of single-taxon assays with the broad taxonomic coverage of metabarcoding is needed. High-throughput quantitative PCR (HT-qPCR) has emerged as one such method that holds particular promise for ecological applications. Here, we present the development and validation of a HT-qPCR biochip (i.e., biochip) protocol for a suite of western North American fishes with high cultural, economic, and ecological significance. Coupling this biochip with systematically distributed eDNA samples collected by citizen scientists, we evaluated the distribution of multiple species throughout a large river basin in the U.S. Pacific Northwest. Patterns of detection using the biochip were comparable to those using single-taxon qPCR (ST-qPCR; mean concordance = 0.917), despite the biochip using 90% less DNA per taxon and reducing costs by an estimated 40%. Comparisons between biochip results and distributions derived from conventional methods demonstrate that this approach not only offers more reliable and high-resolution distribution data for these species but also achieves this more efficiently than traditional sampling methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".