Assessing population maturity of three perennial grass species: Influence of phenology and tiller demography along latitudinal and altitudinal gradients
Bibliographic record
Abstract
Abstract To assess the whole‐sward maturity, which is a primary concern for grassland managers, we studied three forage grass species with contrasting phenology over a range of climatic conditions among sites. We considered two main issues: (i) How is grass population maturity related to population phenology, and is this relation affected by environmental factors? and (ii) Is the sum of temperatures a good index to describe phenological development under contrasting climates? To explore the role of temperature accumulation in species population development, we described the phenological development of populations of three grass species along latitudinal and altitudinal gradients at eight locations across France. We used a numerical index of tiller development to quantify sward morphological structure and discriminate phenological peak from average maturity. We report that phenological development rates were similar among sites for each species, but required fewer growing degree days to start at higher latitudes and altitudes. However, we found that population maturity and phenological peak differed significantly due to among‐site variability in vegetative tiller percentage in whole‐population biomass. Our results underlined the importance of considering tiller distribution among phenological stages, especially tiller development synchrony, together with phenology to assess sward maturity in semi‐natural permanent grasslands.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".