What drives plant species diversity? A global distributed test of the unimodal relationship between herbaceous species richness and plant biomass
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Question For over a century, ecologists have grappled with the question “what drives species diversity?” Urgent global issues such as loss of biodiversity and the relative importance of species richness for ecosystem function and services has heightened the relative importance of understanding processes that control species diversity. Here we present the plans for a global coordinated distributed experiment for herbaceous communities, the H erb D iv N et, to test the hump‐backed model, a unimodal relationship between species richness and aboveground plant biomass plus dead plant litter HBM , to determine whether scale may influence the HBM , and to explore drivers of plant diversity. Location Globally distributed experiment. Methods We propose a nested, standardized sampling design 8 × 8 m, with 1 m 2 plots, taken from multiple site locations along a range of sites varying in primary productivity. Results and Conclusions We welcome others with an interest in using global, standardized, coordinated distributed experiments to explore patterns and processes in herbaceous plant communities to join H erb D iv N et in the search of new insights to drivers of plant species diversity.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it