Integrating simulation of architectural development and source–sink behaviour of peach trees by incorporating Markov chains and physiological organ function submodels into L-PEACH
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
L-PEACH is an L-system-based functional-structural model for simulating architectural growth and carbohydrate partitioning among individual organs in peach (Prunus persica (L.) Batsch) trees. The original model provided a prototype for how tree architecture and carbon economy could be integrated, but did not simulate peach tree architecture realistically. Moreover, evaluation of the functional characteristics of the individual organs and the whole tree remained a largely open issue. In the present study, we incorporated Markovian models into L-PEACH to improve the architecture of the simulated trees. The model was also calibrated to grams of carbohydrate, and tools for systematically displaying quantitative outputs and evaluating the behaviour of the model were developed. The use of the Markovian model concept to model tree architecture in L-PEACH reproduced tree behaviour and responses to management practices visually similar to trees in commercial orchards. The new architectural model along with several improvements in the carbohydrate-partitioning algorithms derived from the model evaluation significantly improved the results related to carbon allocation, such as organ growth, carbohydrate assimilation, reserve dynamics and maintenance respiration. The model results are now consistent within the modelled tree structure and are in general agreement with observations of peach trees growing under field conditions.
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.
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 it