Nitrogen effects and genotypic variation in Cd absorption, translocation, and chemical forms in wheat
Bibliographic record
Abstract
Introduction: Reasonable nitrogen (N) and low grain cadmium (Cd) accumulators can effectively reduce grain Cd content in wheat; however, the underlying mechanism remains unclear. Methods: This study aimed to investigate N effects and genotypic variation in Cd absorption, translocation and chemical forms in low (Chuannong30) and high (Chuanmai88) grain-Cd-accumulating wheat. Pot experiment was arranged in a completely randomized design consisting of two-factors: two soil Cd treatments and six N levels. Results and discussion: , without grain toxicity. Increasing N fertilization improved Cd absorption, translocation and distribution in both genotypes, with a higher effect observed in Chuanmai88, the lower grain Cd content in Chuannong30 may be attributed to low root absorption and translocation from leaf to grain. N fertilization increased almost all Cd chemical forms in the root and leaf, especially under high soil Cd condition, Cd fractions extracted by 80% ethanol were predominant in root and leaf of both genotypes and the concentrations and proportions were also higher in Chuanmai88 than in Chuannong30. Moreover, increasing N fertilization significantly decreased soil pH, increased soil Cd exchange capacity and soil Cd bioavailability, resulting in increased Cd accumulation in plants, Chuanmai88 promoted the activation of the Cd migration in the soil.
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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".