An integrated approach to fast and informative morphological vouchering of nematodes for applications in molecular barcoding
Bibliographic record
Abstract
Molecular surveys of meiofaunal diversity face some interesting methodological challenges when it comes to interstitial nematodes from soils and sediments. Morphology-based surveys are greatly limited in processing speed, while barcoding approaches for nematodes are hampered by difficulties of matching sequence data with traditional taxonomy. Intermediate technology is needed to bridge the gap between both approaches. An example of such technology is video capture and editing microscopy, which consists of the recording of taxonomically informative multifocal series of microscopy images as digital video clips. The integration of multifocal imaging with sequence analysis of the D2D3 region of large subunit (LSU) rDNA is illustrated here in the context of a combined morphological and barcode sequencing survey of marine nematodes from Baja California and California. The resulting video clips and sequence data are made available online in the database NemATOL (http://nematol.unh.edu/). Analyses of 37 barcoded nematodes suggest that these represent at least 32 species, none of which matches available D2D3 sequences in public databases. The recorded multifocal vouchers allowed us to identify most specimens to genus, and will be used to match specimens with subsequent species identifications and descriptions of preserved specimens. Like molecular barcodes, multifocal voucher archives are part of a wider effort at structuring and changing the process of biodiversity discovery. We argue that data-rich surveys and phylogenetic tools for analysis of barcode sequences are an essential component of the exploration of phyla with a high fraction of undiscovered species. Our methods are also directly applicable to other meiofauna such as for example gastrotrichs and tardigrades.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".