Modeling Fruit Tree Architectural Growth, Source–Sink Interactions, and Physiology with L-PEACH
Bibliographic record
Abstract
Modeling source–sink interactions and carbohydrate partitioning in plants requires a detailed model of plant architectural development, in which growth and function of each organ is modeled individually and carbohydrate transport among organs is modeled dynamically. L-PEACH is an L-system-based graphical simulation model that combines supply/demand concepts of carbon partitioning with an L-system model of tree architecture to create a distributed supply/demand system of carbon allocation within a growing tree. The whole plant is modeled as a branching network of sources and sinks, connected by conductive elements. An analogy to an electric network is used to calculate the flow and partitioning of carbohydrates between the individual components. The model can simulate multiple years of tree growth and be used to demonstrate effects of irrigation, crop load, and pruning on architectural development, tree growth, and carbon partitioning. Qualitative model outputs are viewed graphically as the tree “grows” on the computer screen while quantitative output data can be evaluated individually for each organ or collectively for an organ type using the MatLab software.
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".