Computed tomography scanning can monitor the effects of soil medium on root system development: an example of salt stress in corn
Bibliographic record
Abstract
Seeds and young seedlings often encounter high soluble salt levels in the upmost soil layers, impeding vigorous growth by affecting root establishment. Computed tomography (CT) scanning used at low X-ray doses can help study root development in such conditions non-destructively, because plants are allowed to grow throughout the experiment. Using a high-resolution Toshiba XVision CT scanner, we studied corn (Zea mays L.) root growth under optimal and salt-stressed conditions in 3D and on a weekly basis over 3 weeks. Two groups of three corn plants were grown in the controlled environment of a growth chamber, in mid-sized plastic pots filled with sieved and autoclaved sand. Seedlings were subjected to first CT scanning 1 week after seed planting. Our main research objectives concerning root systems were: (i) to quantify structural complexity from fractal dimensions estimated on skeletal 3-D images built from CT scanning data; (ii) to measure growth from volumes and lengths and the derived relative rates and increments, after isolating primary and secondary roots from the soil medium in CT scanning data; and (iii) to assess differences in complexity and growth per week and over Weeks 1-3 for groups of corn plants. Differences between groups were present from Week 1; starting in Week 2 secondary roots were present and could be isolated, which refined the complexity and growth analyses of root systems. Besides expected Week main effects (P < 0.01 or 0.05), Week × Group interaction (P < 0.05 or 0.10), and Group main effects were observed. Graphical, quantitative, and statistical analyses of CT scanning data were thus completed at an unprecedented level, and provided new and important insights regarding root system development. Repeated CT scanning is the key to a better understanding of the establishment in the soil medium of crop plants such as corn and the assessment of salt stress effects on developing root systems, in complexity, volume, and length.
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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) | 0.000 | 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".