Computational Models of Auxin-Driven Patterning in Shoots
Bibliographic record
Abstract
Auxin regulates many aspects of plant development and behavior, including the initiation of new outgrowth, patterning of vascular systems, control of branching, and responses to the environment.Computational models have complemented experimental studies of these processes.We review these models from two perspectives.First, we consider cellular and tissuelevel models of interaction between auxin and its transporters in shoots.These models form a coherent body of results exploring different hypotheses pertinent to the patterning of new outgrowth and vascular strands.Second, we consider models operating at the level of plant organs and entire plants.We highlight techniques used to reduce the complexity of these models, which provide a path to capturing the essence of studied phenomena while running simulations efficiently.P lant development is a multiscale self- organizing process.Self-organization-the emergence of higher-level processes and structures through interaction between lower-level components-occurs across all levels of plant organization, from molecular processes within cells to the patterning of tissues, plant organs, entire plants, and plant communities.An important common factor linking these phenomena is the plant hormone auxin (Sachs 2004;Vieten et al. 2007;Leyser 2011).An understanding of plant development, form, and function thus relies, in a fundamental way, on an understanding of the self-organizing processes involving auxin.Computational models and simulations provide useful insights because selforganizing processes are often difficult to grasp intuitively.Consequently, over the past 15 years they have become tightly integrated into exper-imental research on auxin-related patterning, as reflected in general reviews of the topic (e.g., Shi and Vernoux 2019; Ravichandran et al. 2020), and multiple reviews focused specifically on modeling (Rolland-Lagan and Prusinkiewicz
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".