Bibliographic record
Abstract
Abstract The development of an organism results from complex interactions between biophysical and biochemical processes and is very dynamic. Therefore the mechanisms at play are best studied using computer simulations. The large amount of molecular data on multiple aspects of plant development and advances in plant imaging make it possible to use simulation modelling as a tool to complement experimental studies. For instance, models of gene regulation networks can predict genetic interactions, which can later be tested experimentally. Models of pattern formation in the root and the shoot based on the transport of the plant hormone auxin can simulate the localization of proteins involved in auxin efflux as well as generating realistic profiles of auxin distribution. Models are also starting to incorporate growth and the role of mechanical forces in development, which should provide a link between molecular biology studies and biophysics. Key concepts Plant development is highly dynamic as pattern formation and growth occur concurrently. Modelling studies which can account for observed experimental data suggest genetic interactions in space can generate spatial patterns during plant development in a similar way to reaction‐diffusion mechanisms initially postulated in the middle of the twentieth century. Models of the relation between auxin gradient and root growth suggests the plant hormone auxin acts similarly to a morphogen. In the case of auxin the response to the morphogen may affect the morphogen gradient. This feedback mechanism can generate patterns similar to reaction diffusion and may account for phyllotaxis and leaf vein pattern formation. Experimental data is still needed to link models of auxin‐regulated pattern formation with gene regulatory networks. Mechanical forces affect growth and pattern formation. Methodologies are being developed to model the biomechanics of growth and integrate patterning through chemical and physical processes at various scales of organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".