Linking the physiological parameters of nitrate uptake with root morphology and topology in wheat (<i>Triticum durum</i>) and citrus (<i>Citrus volkameriana</i>) rootstock
Bibliographic record
Abstract
The relationships between net nitrate uptake and root morphology and topology were investigated in wheat (Triticum durum Desf.) and citrus (Citrus volkameriana Ten. & Pasq.) seedlings in which root systems were modified by supplying different nutrient concentrations. Root morphological and topological changes were assessed by the number of root tips, the total root length, and the topological index. The net nitrate uptake process was evaluated during the induction and inhibition "feedback" regulation phases. In wheat seedlings, an increase in the external nitrate concentration, at either high or no phosphate, brought about an increase in number of root tips but did not affect total root length. A similar pattern was observed in citrus seedlings. Citrus seedlings also exhibited a shift in root branching pattern from herringbone to dichotomous with an increase in the external nitrate concentration. Conversely, wheat root topology was unaffected by nutrient treatments. A high level of nitrate supplied to wheat and citrus root systems reduced the full induction of the nitrate uptake rate and cumulative nitrate uptake but increased the half-time of reaching the full induction in net nitrate uptake (t1/2kind). Finally, a negative correlation between number of root tips and t1/2kindwas found in both species, while only in citrus seedlings was a positive correlation between t1/2kindand the topological index observed. The data suggest that root morphology has a greater effect on nitrate uptake than root topology.Key words: root morphology, root topology, net nitrate uptake, Citrus volkameriana, Triticum durum.
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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) | 0.000 | 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".