What ‘unexplored’ means: mapping regions with digitized natural history records to look for ‘biodiversity blindspots’
Bibliographic record
Abstract
We examined global records of accessible natural history voucher collections (with publicly available data and reliable locality data) for terrestrial and freshwater vascular plants, fungi, freshwater fishes, birds, mammals, and herpetofauna (amphibians and reptiles) and highlight areas of the world that would be considered undersampled and sometimes called ‘unexplored’ (i.e., have relatively low, or no evidence of, past sampling efforts) under typical Western-scientific descriptions. We also question what ‘unexplored’ may mean in these contexts and explain how replacing the term in favor of more nuanced phrasing (e.g., ‘biodiversity blindspots,’ which emphasizes the lack of publicly available data about specimens) can mitigate future misunderstandings of natural history science. We also highlight geographic regions where there are relatively few or no publicly available natural history records to raise awareness about habitats that might be worthy of future natural history research and conservation. A major finding is that many of the areas that appear ‘unexplored’ may be in countries whose collections are not digitized (i.e., they don’t have metadata such as GPS coordinates about their voucher specimens publicly available). We call for museums to prioritize digitizing those collections from these ‘biodiversity blindspots’ and for increased funding for museums to aid in these efforts. We also argue for increased scientific infrastructure so that more reference collections with vouchers can be kept in the countries of origin (particularly those countries lacking such infrastructure currently).
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.002 | 0.000 |
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".