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Record W3087515211 · doi:10.5281/zenodo.4453866

Use of fractal analysis principles when describing flavonoids variety of the south trans-urals plants

2020· article· en· W3087515211 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyElutionHigh-performance liquid chromatographyBotanyChromatographyChemistry

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.149
GPT teacher head0.206
Teacher spread0.056 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it