A high-throughput determination of metal concentrations in whole intact<i>Arabidopsis thaliana</i>seeds using synchrotron-based X-ray fluorescence spectroscopy
Bibliographic record
Abstract
The identification of genes involved in metal metabolism in plants requires the 'screening' of thousands of genetic variants. While inductively coupled plasma mass-spectroscopy has been used to identify variants with an altered total metal concentration, a more convenient high-throughput technique capable of examining individual seeds (or other tissues) would be useful. Here, the high brightness of synchrotron radiation has been utilised to examine relative metal concentrations in seeds of the genetically well characterised plant Arabidopsis thaliana. The relative concentrations of Mn, Fe, Ni, Cu and Zn in individual seeds were determined using a 500 microm x 500 microm beam. Metal concentrations were normally distributed, except where metal-containing dust contaminated the samples. Neither seed orientation nor genetic background (from three 'wild type' variants with different genetic lineages) had a significant affect on the Zn-normalised metal concentration. No advantages, such as the observation of tissue-specific metal accumulation, were obtained by using a 50 microm x 50 microm beam. A high-throughput proof-of-concept experiment was demonstrated that could be used to screen libraries of genetic variants for individuals with altered metal concentrations. Further work is required to standardise the technique before screening of libraries is possible.
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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".