Herbarium collections remain essential in the age of community science
Bibliographic record
Abstract
The past decade has yielded more biodiversity observations from community science than the past century of traditional scientific collection. This rapid influx of data is promising for overcoming critical biodiversity data shortfalls, but we also have vast untapped resources held in undigitized natural history collections. Yet, the ability of these undigitized collections to fill data gaps, especially compared against the constant accumulation of community science data, remains unclear. Here, we compare how well community science (iNaturalist) observations and digitized herbarium specimens represent the diversity, distributions, and modeling needs of vascular plants in Canada. We find that, despite having only a third as many records, herbarium specimens capture more taxonomic, phylogenetic, and functional diversity and more efficiently capture species' environmental niches. As such, the digitization of Canada's 7.3M remaining specimens has the potential to more than quintuple our ability to model biodiversity. In contrast, it would require over 27M more iNaturalist observations to produce similar benefits. Our findings indicate that digitizing Earth's remaining herbarium specimens is likely an efficient, feasible, and potentially critical investment when it comes to improving our ability to predict and protect biodiversity into the future.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".