Use of fractal analysis principles when describing flavonoids variety of the south trans-urals plants
Bibliographic record
Abstract
neutral theory have shown the statistically insignificant dependence of various biological processes on the environment (Kimura 1983; Hubbell 2006; Dembitsky et al. 2007; Morozov et al. 2014, Rosenberg 2013). This calls for an attempt to apply neutralist approaches to the data arrays as presented herein. Fractal analysis is a highly generalized concept of the stochastic emergence of self-similar structures (McGill 2010; Gelashvili et al. 2013). Given that the accumulation of flavonoid in plants in South Trans-Urals is in a weak correlation with environmental factors while the chromatographic spectra of single plant specimens show inter-specimen and interpopulation diversity, this research effort seeks to assess the applicability of fractal analysis to the biosynthesis of flavonoids (Amineva 2003; Ableeva 2004,Scherbakov et al. 2009, 2012, 2013).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".