Modeling Trigger Evolution in Biophysical Invasions Based on the Situational Choice of Hybrid Computing
Bibliographic record
Abstract
Abstract Original modeling methods are proposed to study nonequilibrium processes in biophysics. Modifications carried out only by expanding the model dimension do not yield the desired results compared with the behavior of invasions and epidemics. According to our idea, the construction of modeling systems of nonlinear equations should be close to real abrupt changes and take into account rapid changes during evolutionary adaptation. A predictive model should take into account special properties for each individual current situation. The evolutionary process in an opposing community of organisms is never limited to the adaptation of only one component of a biophysical system. Invasions serve as a catalyst for evolution. Of particular importance to us is the special case of invasion characterized as the launch of epidemics of viruses new to the population. The coronavirus epidemic is continuing in 2024 with an autumn COVID wave in Europe, Australia, and the United States. The dynamics of morbidity in the regions is again different. New strains with signs of convergent changes, the XDV and aggressive XEC, appear. Currently, the main generator of the Omicron mutation accumulation is the wide BA.2/JN/KP branch. In 2025, the situation with the leadership of coronavirus variants will certainly change; therefore, modeling of new COVID waves will again require corrections to the models. These properties could not be described in SIRS epidemic model variants, as well as the unexpectedly re-emerging mpox outbreak. The situation with the constant presence of virus variants and local waves in the population is not the worst scenario. A repeat of the pandemic wave 5 years after the end of the epidemic and weakening of the population immunity are much worse. Invasive processes in biosystems, when species with a high reproductive parameter are introduced into a new range, trigger unpredictable and diverse nonlinear processes. In the trophic chains of biosystems, the effects of invasions spread sharply, not as in the situation with a systematic expansion of ranges. Some biophysical invasions develop rapidly in the form of an outbreak from a single peak. After an extreme maximum, a state of prolonged depression of the invasive species or chronicity of a virus in the body often develops. The crisis is caused by destruction of the invasion of its own breeding environment. Many dangerous invasive phenomena pulsate and last for decades, like the invasion of the gypsy moth in the forests of Canada. Based on the problems of biophysics, a conveniently modifiable and supplemented structure of auxiliary equations with event redefinitions is proposed. Outbreaks of reproduction of invasive species are modeled by different equations at the stages of development. To model situations of outbreaks of various insect populations, different forms of immunity regulation are combined in the model and a technique for constructing a hybrid model with complemented equations is developed. The survival equations are related to the growth equations with a synchronized algorithm for redefining the computational structure. A threshold scenario of a dangerous pulsating biophysical invasion is obtained from three equations for the loss of generations. In the scenario experiments, the hybrid model is able to describe the long-period threshold population wave effects for locally observed decaying pulsating outbreaks of aggressive species invading an adaptive environment. The outbreaks end in long fluctuations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".