Pigment Concentrations among Heat-tolerant Turfgrasses
Bibliographic record
Abstract
Heat-tolerant bluegrass varieties were developed to resist dormancy and retain pigmentation during heat stress events. The objective of this study was to investigate the influence of grass species, nitrogen (N) fertilization, and seasonality on the accumulation patterns of lutein, β-carotene, and chlorophyll a and b in the leaf tissues of turfgrass. The heat-tolerant bluegrass cultivars Dura Blue and Thermal Blue ( Poa pratensis L. × Poa arachnifera Torr.), Apollo kentucky bluegrass ( Poa pratensis L.), and Kentucky 31 tall fescue ( Festuca arundinacea Schreb.) were compared for the accumulation of plant pigments. Evaluations were conducted over 2 consecutive years (Years 4 and 5 after establishment) during two different seasons (spring and summer) and under varying N fertilization. Fertilizer applications of 5, 14, and 27 g N/m 2 /year resulted in a significant positive correlation for the accumulation of leaf blade lutein and chlorophyll a and b , but not for β-carotene. The accumulation of the four measured plant pigments among the grasses was significantly different with ‘Apollo’ having the largest concentration of pigments followed by ‘Dura Blue’, ‘Thermal Blue’, and finally ‘Kentucky 31’. Specifically, when comparing the cultivars Apollo and Kentucky 31, the pigment levels decreased 27%, 26%, 26%, and 23% for lutein, β-carotene, and chlorophyll a and b , respectively. The interesting observation of the analysis of the grass pigment concentrations was that the least reported heat-tolerant cultivar in our study (‘Apollo’) had the largest measured pigment concentrations.
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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".