Prioritizing taxa for genetic reference database development to advance inland water conservation
Bibliographic record
Abstract
Biodiversity loss has accelerated over the past century and freshwater species overall are among those experiencing greatest declines. Genetic resources have the potential to help evaluate the full magnitude of this loss and represent a key tool to effectively allocate conservation resources and monitor the success of restoration efforts. The full power of genetic resources will be realized when the daunting task of referencing all DNA sequences of freshwater organisms is complete. Here, we quantified the availability and distribution of barcode and genome data for freshwater macroscopic organisms in Canada, a country rich in inland water resources and thus particularly vulnerable to aquatic species losses. Impressively, most inland water species (86 %) were represented by barcodes recorded in the BOLD Systems database, while very few had full genomes available (<4 %) in the NCBI database. We identified barcode data deficiencies in northern regions and for taxa assessed as most at risk or without sufficient information for conservation status classification. As expected, the speciose insect group had a lower-than-average number of records per species and a high proportion of data deficient species without adequate barcode coverage. This study highlights where future sequencing resources should be prioritized within initiatives such as the Canada BioGenome Project and BIOSCAN Canada and provides a workflow that could be applied internationally to inform conservation management plans and to mitigate biodiversity loss.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".