Morphology and fractal dimension of root systems of maize hybrids bearing the leafy trait
Bibliographic record
Abstract
A rapid and accurate assessment of root morphological characteristics can be important for identifying promising plant genotypes. Scanner-based image analysis has made the measurement of root morphology faster, more accurate, and less subjective, and allows for estimation of fractal dimension (FD). We used scanner-based image analysis to investigate root morphology in leafy vs. conventional maize (Zea mays) genotypes. Two experiments were conducted following a completely randomized design with ten and five replicates. In the first experiment, treatments were factorial combinations of four hybrids (leafy reduced-stature (LRS), non leafy-reduced stature (NLRS), leafy normal stature (LNS), and non leafy normal stature (conventional commercial) hybrid, Pioneer 3979 (P3979)), and four sampling dates (5, 10, 15, and 20 days from transfer to growth pouches). In the second experiment, plants were grown for 15 days from emergence in 10-L plastic pots. This experiment included three maize genotypes (LRS, LNS, and Pioneer 3905 (P3905)). The FD of young root systems was similar for leafy genotypes (FD [Formula: see text] 1.66) and was higher for P3905 (FD [Formula: see text] 1.74). The FD correlated with root characteristics generally included in root growth models (0.68 [Formula: see text] r [Formula: see text] 0.87). Thus, FD could be used to summarize a number of root morphology variables.Key words: root morphology, fractal dimension, root branching, image analysis, maize.
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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".