A can of worms: estimating the global number of earthworm species
Bibliographic record
Abstract
Abstract Estimating the overall species number for a given taxon is a central issue in ecology and conservation biology. This is especially topical for soil organisms, which comprise most known species but whose taxonomy remains largely understudied. Here, we estimated the global number of earthworm species based on the Joppa approach, which models taxonomic effort over time to estimate the total number of known and as yet unknown species in a given taxa. Our Bayesian estimation of the Joppa model suggests a global diversity of the order of 30,000 species, suggesting that the 5,679 earthworm taxa already described only represent around 20% of the actual global species diversity. However, the uncertainty around this estimate is considerable due to severe undersampling and as the model cannot unambiguously decide whether we are describing few species because of a small pool of as yet unknown species, or because of a lack of taxonomic efficiency. Considering the current rate of new species description, we calculate that it would take at least 120 years to describe all the earthworm species existing on Earth, and we discuss thedifferent strategies that should be developed to facilitate and accelerate the discovery and naming of species new to science.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".