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 \nprograms. Probe based real-time PCR (qPCR) provides an accurate, cost-effective and high-throughput \nmethod for species identification. Here we present the development and validation of species-specific \nprimers and probe for the identification of eight freshwater fish species. The development of a fully \nautomated species-decoder algorithm allowed for target species identification with 100% accuracy while \ncompletely removing any false-positive detection of non-target species. Furthermore, the probe-based \nqPCR technique utilized in this study is substantially more cost-effective and time efficient than DNA \nbarcoding and morphological identification methods. The qPCR assays were also highly sensitive and \naccurately detected target species from collected environmental DNA (eDNA) samples. In summary, \nprobe-based multiplex qPCR assays provide a rapid and accurate method for freshwater fish species \nidentification and the methodology established in this study can be utilized for various other species \nidentification initiatives.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".