Nitrogen isotope discrimination as an integrated measure of nitrogen fluxes, assimilation and allocation in plants
Bibliographic record
Abstract
Fractionation of nitrogen isotopes between a plant and its environment occurs during uptake and assimilation of inorganic nitrogen. Fractionation can also occur between roots and the shoot. Under controlled nitrogen conditions, whole-plant and organ-level nitrogen isotope discrimination (Δ(15) N) is suggested to primarily be a function of three factors: nitrogen efflux back to the substrate relative to gross influx at the root (efflux/influx), the proportion of net influx assimilated in the roots and the export of remaining inorganic nitrogen for assimilation in the leaves. Here, an isotope discrimination model combining measurements of δ(15) N and nitrogen content is proposed to explain whole-plant and organ-level variation in δ(15) N under steady-state conditions and prior to any significant retranslocation. We show evidence that nitrogen isotope discrimination varies in accordance with changes to nitrogen supply or demand. Increased whole-plant discrimination (greater Δ(15) N or more negative δ(15) N relative to the source nitrogen δ(15) N) indicates increased turnover of the cytosolic inorganic nitrogen pool and a greater efflux/influx ratio. A greater difference between shoot and root δ(15) N indicates a greater proportion of inorganic nitrogen being assimilated in the leaves. In addition to calculations of integrated nitrogen-use traits, knowledge of biomass partitioning and nitrogen concentrations in different plant organs provides a spatially and temporally integrated, whole-plant phenotyping approach for measuring nitrogen-use in plants. This approach can be used to complement instantaneous cell- and tissue-specific measures of nitrogen use currently used in nitrogen uptake and assimilation studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".