Research Infrastructures offer capacity to address scientific questions never attempted before: Are all taxa equal?
Bibliographic record
Abstract
Research Infrastructures (RIs) are facilities, resources and services used by the scientific community to conduct research and foster innovation. LifeWatch ERIC has developed various virtual research environments, which include many virtual laboratories (vLabs) offering high computational capacity and comprehensive collaborative platforms that supporting the needs of digital biodiversity science. Over its 250 years of history, the taxonomic research community has developed a system for describing, classifying and naming taxa across multiple levels. For the marine biota, taxonomic information is organized and made publicly available through the World Register of Marine Species (WoRMS) that records more than 250,000 described valid species. Although scientists tend to assign an equal status (in terms of contribution to overall diversity) to each taxon used in taxonomy, biogeography, ecology and biodiversity, the question “ are all taxa equal? ” has never been tested at a global scale. We present evidence that this question can be addressed by applying relatedness indices (Taxonomic Distinctness) over the entire WoRMS metazoan tree. The RvLab, developed by the LifeWatchGreece RI, operating on a high-performance computer cluster, has been used to meet the high computational demands required for such an analysis at a global scale.
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".