Nitrogen isotope discrimination in open-pollinated and hybrid canola suggests indirect selection for enhanced ammonium utilization
Bibliographic record
Abstract
Nitrogen isotope discrimination (Δ15N) may have utility as an indicator of nitrogen use in plants. A simple Δ15N-based isotope mass balance (IMB) model has been proposed to provide estimates of efflux/influx (E/I) ratios across root plasma membranes, the proportion of inorganic nitrogen assimilation in roots (Proot) and translocation of inorganic nitrogen to shoots (Ti/Tt) under steady-state conditions. We used the IMB model to investigate whether direct selection for yield in canola (Brassica napus L.) has resulted in indirect selection in traits related to nitrogen use. We selected 23 canola lines developed from 1942 to 2017, including open-pollinated (OP) lines developed prior to 2005 as well as more recent commercial hybrids (CH), and in three separate experiments grew them under hydroponic conditions in a greenhouse with either 0.5 mM ammonium, 0.5 mM nitrate, or 5 mM nitrate. Across all lines, E/I, Proot and Ti/Tt averaged 0.09±0.03, 0.82±0.05 and 0.23±0.06 in the low nitrate experiment, and 0.31±0.06, 0.71±0.07 and 0.42±0.12 in the high nitrate experiment, respectively. In contrast, in the ammonium experiment average E/I was 0.40±0.05 while Ti/Tt averaged 0.07±0.04 and Proot averaged 0.97±0.02. Although there were few consistent differences between OP and CH under nitrate nutrition, commercial hybrids were collectively better able to utilize ammonium as their sole nitrogen source, demonstrating significantly greater overall biomass and a lower Proot and a higher Ti/Tt, suggesting a somewhat greater flux of ammonium to the shoot. Average root and whole-plant Δ15N were also slightly higher in CH lines, suggesting a small increase in E/I. An increased ability to tolerate and/or utilize ammonium in modern canola hybrids may have arisen under intensive mono-cropping.
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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 | 0.001 | 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) | 0.001 | 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".