Molecular techniques for the identification of freshwater fish species for environmental monitoring programs
Bibliographic record
Abstract
Reliable species identification methods are important for industrial environmental monitoring programs. Probe based real-time PCR (qPCR) provides an accurate, cost-effective and high-throughput method for species identification. Here we present the development and validation of species-specific primers and probe for the identification of eight freshwater fish species. The development of a fully automated species-decoder algorithm allowed for target species identification with 100% accuracy while completely removing any false-positive detection of non-target species. Furthermore, the probe-based qPCR technique utilized in this study is substantially more cost-effective and time efficient than DNA barcoding and morphological identification methods. The qPCR assays were also highly sensitive and accurately detected target species from collected environmental DNA (eDNA) samples. In summary, probe-based multiplex qPCR assays provide a rapid and accurate method for freshwater fish species identification and the methodology established in this study can be utilized for various other species identification initiatives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".