Developmentally equivalent tissue sampling based on growth kinematic profiling of Arabidopsis inflorescence stems
Bibliographic record
Abstract
• Directional growth in Arabidopsis thaliana during bolting of the inflorescence stem makes this an attractive system for study of the underlying processes of tissue elongation and cell wall extension. Analysis of local molecular events accompanying Arabidopsis inflorescence stem elongation is hampered by difficulties in isolating developmentally matched tissue samples from different plants. • Here, we present a novel sampling approach in which specific developmental stages along the developing stem are defined nonintrusively in terms of their relative elemental growth rate by use of time-lapse imagery and subsequent derivation of growth kinematic profiles for individual plants. • Growth kinematic profiling reveals that key developmental transitions such as the point of maximum elongation rate and the point of cessation of elongation occur over broad and overlapping ranges across individuals within a population of the Columbia (Col-0) ecotype. The position of these transitions is only weakly correlated with overall plant height, which undermines the common assumption that physically similar plants have closely matched growth profiles. • This kinematic profiling approach provides high-resolution growth phenotyping of the developing stem and thereby enables the harvest, pooling and analysis of developmentally matched tissue samples from multiple Arabidopsis plants.
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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".