The macroecology of knowledge: Spatio-temporal patterns of name-bearing types in biodiversity science
Bibliographic record
Abstract
Ecological and evolutionary processes are recognized as the main factors generating and maintaining biodiversity. However, how biodiversity knowledge is collated, organized, and distributed worldwide influences our perceptions and inferences about biodiversity and the underlying processes. We demonstrated that name-bearing type specimens (NBT), the most fundamental reference for the identity of any species, of all freshwater and brackish fish species in the world are mostly housed in museums in Global North countries. The unequal distribution of NBT results from historical and socioeconomic factors and has implications for both the Global North and South countries. For the Global North, which concentrates most of NBT, we found a mismatch between NBT housed in their ichthyological collections and their native biotas. On the other hand, countries with most NBT of their native species housed elsewhere face a barrier in advancing biodiversity research due to the difficulty in accessing reference material, hampering global efforts in cataloging, reviewing, and describing new species. We advocate that if we are truly committed to advancing biodiversity research, we should pursue global initiatives to make the distribution of biological knowledge fairer among countries, which involves programs for specimen repatriation and facilitation of accessibility of NBT material to researchers from the countries in which they were collected.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".