The patterns of vascular plant discoveries in China
Bibliographic record
Abstract
ABSTRACT Botanical discovery has a long journey of revelation that contributes unparalleled knowledge to shape our understanding about nature. Plant discovery in China is an immanent part of that journey. To understand the patterns of plant discoveries in China and detect which taxa and areas harbor most numbers of undiscovered species, we analyzed the discovery times of 31093 vascular plant species described in Flora of China . We found that species with larger range size and distributed in northeastern part of China have a higher discovery probability. Species distributed on the coast were discovered earlier than inland species. Trees and shrubs of seed plants have the highest discovery probability and ferns have the lowest discovery probability. Herbs hold the largest number of undiscovered species in China. Most undiscovered species are found in southwest China, where three global biodiversity hotspots locate. Spatial patterns of mean discovery year and inventory completeness are mainly driven by the total number of species and human population density in an area and whether the area is coastal or not. Our results showed that socio-economic factors dictate the discovery patterns of vascular plants in China. Undiscovered species are mostly narrow-ranged, inconspicuous endemic such as herbs, which are prone to extinctions and locate in biodiversity hotspots in southwest China. We suggest that the future effort on plant discovery in China be prioritized in southwest China.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".