The hidden biodiversity knowledge split in biological collections
Bibliographic record
Abstract
Ecological and evolutionary processes generate biodiversity, yet how biodiversity data are organized and shared globally can shape our understanding of these processes. We show that name-bearing type specimens-the primary reference for species identity-of all freshwater and brackish fish species are predominantly housed in Global North museums, disconnected from their countries of origin. This geographical divide creates a 'knowledge split' with consequences for biodiversity science, particularly in the Global South, where researchers face barriers in studying native species' name bearers housed abroad. Meanwhile, Global North collections remain flooded with non-native name bearers. We relate this imbalance to historical and socioeconomic factors, which ultimately restrict access to critical taxonomic reference materials and hinder global species documentation. To address this disparity, we call for international initiatives to promote fairer access to biological knowledge, including specimen repatriation, improved accessibility protocols for researchers in countries where specimens originated and inclusive research partnerships.
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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 | 0.040 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".