Bibliographic record
Abstract
The fact that temperate grasslands often contain upwards of 30 vascular plant species per m(2) yet these species seem to have relatively similar life histories and resource requirements has made explaining species coexistence in these communities a major focus of research. While the reduction of competition by disturbance has been a popular explanation for species coexistence, in tallgrass prairies any level of disturbance either has no effect, or decreases diversity, since it favors the dominant plants. Although there has long been speculation that grassland species could coexist by niche partitioning the concept received renewed interest when it was shown that soil hydrology could explain species coexistence. One aspect of community structure that has not been explained by niche partitioning is the rareness and commonness of species within communities. There are three classes of explanations for rareness: narrow habitat requirements, low competitive ability combined with frequency dependent fitness and, dispersal ability. However, evidence for these explanations tend to be anecdotal, focusing on particular species. Here I show that in tallgrass prairies common and rare species consistently occupy different parts of niche space, with rare species being restricted by the cover of common species and occupying the rare available niches.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".