Divergent effects of defoliation intensity and frequency on tiller growth and production dynamics of <i>Pascopyrum smithii</i> and <i>Hesperostipa comata</i>
Bibliographic record
Abstract
Abstract Rotational stocking theoretically enables regrazing of regrowth, but regrowth may be limited in xerophytic vegetation. We tested the hypothesis that regrowth would be slow and fixed in Hesperostipa comata (a caespitose, drought‐tolerant grass), while growth would be flexible in Pascopyrum smithii (a rhizomatous, less drought‐tolerant grass) under increasing defoliation and moisture, by assessing tiller growth rates, population dynamics and plant yield on marked plants in a Dry Mixed Grass prairie. Plots were clipped in late summer to simulate a deferred control, or intermittently during the growing season (May–August) at high intensity–low frequency (HILF), low intensity–high frequency (LIHF) or high intensity–high frequency and crossed with two water treatments (ambient and addition) in both a xeric upland and a mesic lowland. Growing season defoliation increased tiller growth rates of P. smithii in the upland, whereas HILF and LIHF reduced growth in the lowland. All defoliation regimes increased tiller growth for H. comata. Tiller populations of H. comata increased with frequent defoliation, while tiller populations of P. smithii decreased regardless of defoliation regime. Frequent defoliation, regardless of intensity, reduced yield relative to the deferred control for both grasses. While water addition consistently increased growth rates and reduced differences in tiller number among defoliation treatments, the regrowth of both grasses remained similar under varied defoliation with ambient moisture. High moisture conditions also promoted regrowth more in P. smithii compared to H. comata. In summary, neither grass species displayed an inherently fixed or flexible tiller or plant yield response in response to defoliation or moisture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".