TiP-Leaf: A dataset of leaf traits across vegetation types on the Tibetan Plateau
Bibliographic record
Abstract
Abstract. Functional trait databases are emerging as a crucial tool for a wide range of ecological studies including the next-generation vegetation modeling across the world. However, few large-scale studies have been reported on plant traits in the Tibetan Plateau (TP), the cradle of East Asian flora and fauna with specific alpine ecosystems, no report on plant trait databases could be found. Here an extensive dataset of 11 leaf functional traits (TiP-Leaf) for mainly herbs and shrubs and a few trees on the TP was compiled through field surveys. The TiP-Leaf dataset, compiled from 336 sites distributed mainly in the plateau surface and the northern margin of the TP across alpine and temperate vegetation regions and sampled from 2018 to 2021, contains 1692 morphological trait measurements of leaf thickness, leaf fresh weight, leaf dry weight, leaf dry-matter content, leaf water content, leaf area, specific leaf area and leaf mass per area and 1645 chemical element trait measurements of leaf carbon, nitrogen and phosphorus contents. Thus, 468 species belonging to 184 genera and 51 families were obtained and measured. In addition to leaf trait measurements, geographic coordinates, bioclimate variables, disturbance intensity and vegetation types of each site were also recorded. The dataset could provide solid data support for effectively quantifying the modern ecological features of alpine ecosystems, further evaluating the response of alpine ecosystem to climate change and human disturbances and improving the next-generation vegetation model. It could be a great contribution to the regional and global plant trait databases. The dataset is available from the National Tibetan Plateau Data Center (TPDC; Jin et al., 2022; https://doi.org/10.11888/Terre.tpdc.272516).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".