Lossy compression of tree architectures
Bibliographic record
Abstract
Plants usually show intricate structures whose representation and management are an important source of complexity of models. Yet plant structures are also repetitive: although not identical, the organs, axes, and branches at different positions are often highly similar. From a formal perspective, this repetitive character of plant structures was first exploited in fractal-based plant models (Barnsley, 2000; Ferraro et al., 2005; Prusinkiewicz and Hanan, 1989; Smith, 1984). In particular, L-systems have extensively been used in the last two decades to amplify parsimonious rule-based models into complex branching structures by specifying how fundamental units are repeatedly duplicated and modified in space and over time (Prusinkiewicz et al., 2001). However, the inverse problem of finding a compact representation of a branching structure has remained largely opened, and is now becoming a key issue in modeling applications as it needs to be solved to both get insight into the complex organization of plants and to decrease time and space complexity of simulation algorithms. The idea is that a compressed version of a plant structure might be much more efficient to manipulate than the original extensive branching structure. For instance, Soler et al. (2003) have shown that the complexity of radiation simulation can be drastically reduced if self-similar representations of plants are used. Unfortunately, strict self-similarity has a limited range of applications, because neither real plants nor more sophisticated plant models are exactly self-similar. Consequently, we propose in this paper an algorithm that exploits approximate self-similarity to compress plant structures to various degrees, representing a tradeoff between compression rate and accuracy. This new compression method aims at making possible to efficiently model, simulate and analyze plants using compressed representations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".