Genetic calibration of species diversity among North America's freshwater fishes
Bibliographic record
Abstract
Freshwater ecosystems are being heavily exploited and degraded by human activities all over the world, including in North America, where fishes and fisheries are strongly affected. Despite centuries of taxonomic inquiry, problems inherent to species identification continue to hamper the conservation of North American freshwater fishes. Indeed, nearly 10% of species diversity is thought to remain undescribed. To provide an independent calibration of taxonomic uncertainty and to establish a more accessible molecular identification key for its application, we generated a standard reference library of mtDNA sequences (DNA barcodes) derived from expert-identified museum specimens for 752 North American freshwater fish species. This study demonstrates that 90% of known species can be delineated using barcodes. Moreover, it reveals numerous genetic discontinuities indicative of independently evolving lineages within described species, which points to the presence of morphologically cryptic diversity. From the 752 species analyzed, our survey flagged 138 named species that represent as many as 347 candidate species, which suggests a 28% increase in species diversity. In contrast, several species of parasitic and nonparasitic lampreys lack such discontinuity and may represent alternative life history strategies within single species. Therefore, it appears that the current North American freshwater fish taxonomy at the species level significantly conceals diversity in some groups, although artificially creating diversity in others. In addition to providing an easily accessible digital identification system, this study identifies 151 fish species for which taxonomic revision is required.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".