A Streamlined System for Species Diagnosis in Caenorhabditis (Nematoda: Rhabditidae) with Name Designations for 15 Distinct Biological Species
Bibliographic record
Abstract
The rapid pace of species discovery outstrips the rate of species description in many taxa. This problem is especially acute for Caenorhabditis nematodes, where the naming of distinct species would greatly improve their visibility and usage for biological research, given the thousands of scientists studying Caenorhabditis. Species description and naming has been hampered in Caenorhabditis, in part due to the presence of morphologically cryptic species despite complete biological reproductive isolation and often enormous molecular divergence. With the aim of expediting species designations, here we propose and apply a revised framework for species diagnosis and description in this group. Our solution prioritizes reproductive isolation over traditional morphological characters as the key feature in delineating and diagnosing new species, reflecting both practical considerations and conceptual justifications. DNA sequence divergence criteria help prioritize crosses for establishing patterns of reproductive isolation among the many species of Caenorhabditis known to science, such as with the ribosomal internal transcribed spacer-2 (ITS2) DNA barcode. By adopting this approach, we provide new species name designations for 15 distinct biological species, thus increasing the number of named Caenorhabditis species in laboratory culture by nearly 3-fold. We anticipate that the improved accessibility of these species to the research community will expand the opportunities for study and accelerate our understanding of diverse biological phenomena.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.015 |
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".