Comparison of larval fish detections using morphology-based taxonomy versus high-throughput sequencing for invasive species early detection
Bibliographic record
Abstract
When first introduced, invasive species typically evade detection; DNA barcoding coupled with high-throughput sequencing (HTS) may be more sensitive and accurate than morphology-based taxonomy, and thereby improve invasive (or rare) species detection. We quantified the relative error of species detection between morphology-based and HTS-based taxonomic identification of ichthyoplankton collections from the Port of Duluth, Minnesota, an aquatic non-native species introduction 'hot-spot' in the Laurentian Great Lakes. We found HTS-based taxonomy identified 28 species and morphology-based taxonomy 30 species, of which 27 were common to both. Among samples, 76% of family-level taxonomic assignments agreed; however, only 42% of species assignments agreed. Most errors were attributed to morphology-based taxonomy, whereas HTS-based taxonomy error was low. For this study system, for most non-native fishes, the detection probability by randomized survey for larvae was similar to that by a survey that is optimized for non-native species early detection of juveniles and adults. We conclude that classifying taxonomic errors by comparing HTS results against morphology-based taxonomy is an important step toward incorporating HTS-based taxonomy into biodiversity surveys.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".