COMPETITION, RESOURCES, AND VEGETATION DURING 10 YEARS IN NATIVE GRASSLAND
Bibliographic record
Abstract
A 10-year experiment tested for variation in competition intensity over time in a natural grassland at the northern edge of the Great Plains. Growing-season precipitation varied fivefold during the study. All ecosystem-level variables varied significantly among years, and most covaried in expected ways. The covers of all common grasses possessing the C3 photosynthetic pathway varied significantly among years; in contrast, all common species with traits associated with drought tolerance (a C4 grass, a lichen, a spikemoss, and a subshrub) did not vary. Annual transplant experiments measured the competitive effects of neighbors on the growth of individuals of the native grass Bouteloua gracilis. A significant interaction between year and competition showed that competition intensity varied among years. The size of this effect, however, was small (eta2 = 0.074) relative to the size of the direct effect of competition (eta2 = 0.20) or the year in which the experiment was conducted (eta2 = 0.51). Further, competition intensity was not significantly related to any variable describing standing crop or resources, or species richness. Species richness was highest in years with high precipitation, standing crop, and individual growth, due to the recruitment of rare species that were absent from dry years. In summary, variation in competition intensity was statistically significant but had small effects relative to the direct effects of climate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".