Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth
Bibliographic record
Abstract
The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.
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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".