Comparing the ability of natural history collections and community science observations to estimate plant niches
Bibliographic record
Abstract
Together, the ongoing digitization of Earth’s natural history collections alongside the rapid rise of community science is producing a windfall of biodiversity data. Considering projected growth in these traditional and novel data streams, understanding their ability to represent the diversity and distribution of Earth’s species is critical to maximizing their usefulness to research and conservation. Here, we test the ability of natural history collections and community science to describe the environmental niches of vascular plants. We find that the physical plant specimens comprising Earth’s herbarium collections capture, on average, 2.3 times more niche space compared to observations made through the common community science platform iNaturalist. Across space, herbarium records capture more niche space for the average plant in every botanical region on Earth, indicating these collections remain essential to our understanding of global plant distributions. That said, having only existed for the past decade, iNaturalist has rapidly amassed an impressive number of observations that have expanded our estimates of plant niches by 9% for the average species. Taken together, these results highlight the growing contribution of community science initiatives like iNaturalist to our knowledge of biodiversity while reaffirming the critical value of herbaria and their collections to modern scientific research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".