Phosphorous Uptake and Concentration of Timothy Genotypes under Varying N Applications
Bibliographic record
Abstract
ABSTRACT Improving P uptake in timothy (Phleum pratense L.) would reduce excess soil phosphate while producing forage with greater P nutritional value. A strong relationship exists between N and P concentration in plants, and we hypothesized that genotypes characterized for contrasting N concentration or uptake may also exhibit contrasting P concentration and uptake. Two timothy populations derived from divergent selection for high and low forage N concentration were studied in Experiment 1. These two populations were also studied in Experiment 2, along with seven half‐sib families identified in a field study as having contrasting dry matter (DM) yield and N concentration. In both experiments, a reference population, ‘Champ’, was included and plants were grown in a growth room with varying N rates. Independent of applied N, the genotypes differed for P concentration, P uptake, and P to N ratio (P/N), in forage and total biomass, as well as for leaf P concentration, leaf weight ratio (LWR), root weight ratio (RWR) and efficiency of P uptake for each unit of root biomass (PUPEroot). Averaged across N rates in Experiment 2, forage P concentration among genotypes ranged from 4.1 to 5.1 g P kg−1 DM, while forage P uptake ranged from 10.0 to 14.6 mg P per plant. Variations in P uptake and concentration were related to genotypic differences in DM yield, leaf P concentration, LWR, and RWR. Phosphorus concentration and uptake decreased under N stress. We conclude that variation exists among timothy genotypes for P concentration and uptake.
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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".