Characterizing a new species of Nematoda using genetic and morphological analyses
Bibliographic record
Abstract
Nematodes (Nematoda) are slim tubular worms ranging between 0.5 mm – 2 mm in length and 10 to 100 µm thick. They have effectively adapted to inhabit all regions of the Earth, but are most commonly found in soils, decomposing vegetation, and freshwater sources. Ceanorhabditis elegans (C. elegans), an important member of this phylum, is a valuable model system. Owing to its small, fully sequenced genome, it is typically used to model the development of some diseases, such as neurodegenerative diseases. Nematodes are highly diverse, with over 30,000 species having not yet been described. While C. elegans will continue to be the primary model species, the classification of previously unknown species is valuable as it allows for study of the evolutionary pathway leading to each species, behavior and instincts, and how such animals behave as parasites. This diversity is exciting, and Drs. Kimberly Dej and Bhagwati Gupta work with students to document new species. In the laboratory, we use morphological analysis of the mouth, the pharynx, and the tail, combined with data generated by sequencing the 18S small ribosomal subunit rRNA gene to explore and document these new species. Here, we discuss how it was determined that a unique specimen collected from the Hamilton, Ontario area was found to have features of multiple genera: Oscheius and Ceanoreabditis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".