Globally aggregated biodiversity data impact predictive and descriptive research
Bibliographic record
Abstract
Here, we present an analysis of the growth and use of the Global Biodiversity Information Facility (GBIF) over the last 5 y. GBIF is the world's largest data integrator for biodiversity information and plays a central role in research across the biodiversity and evolutionary science community. With the help of a comprehensive bibliographic dataset comprising 12,193 studies that used GBIF-mediated data, we demonstrate how the global scientific community utilizes the continuously fast-growing amount of open and Findable, Accessible, Interoperable, and Reusable biodiversity data in their research. Overall, more researchers engage with GBIF data, a potential consequence of the rising demands of more global environmental assessments, where GBIF-mediated data are being used as a key resource for biodiversity research. Studies utilizing species distribution modeling were most prevalent and data used for topics related to challenges of the Anthropocene (conservation, climate change, invasive, and pest species). More studies used observational data records, a category that also includes a substantial amount of citizen science data. Our data show that a thematic diversification of GBIF-using literature is accompanied by a rapid diversification of both the additional datasets that GBIF data are analyzed with, as well as the new analytical approaches taken by researchers. This emphasizes the growing importance of GBIF's data infrastructure and services which support global sciences and reflect major shifts in applied science which dictate the need for GBIF and similar data infrastructures to evolve rapidly in order to maintain relevance for research.
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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 | 0.039 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.043 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".