Data-intensive modeling of forested ecosystems
Why this work is in the frame
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Bibliographic record
Abstract
In the present work, we have performed various applied mathematics techniques to model forested ecosystems in North America using Quebec and USA forest inven- tory data. We start with autoregressive models which are analytically tractable and operate with continuous state space. We perform time series statistical analysis of Quebec forest data recorded in 1970–2007. We have obtained that geometric random walk with normal increments adequately describes dynamics of forest biomass yearly averages. For individual forest locations, the best fit also turns out to be geometric random walk, however, the normality tests for residuals fail. We fulfilled the same analysis at the level of USA ecological regions, where we noticed the same pattern in the absolute majority of ecoregions. The exception was California Coastal Province, where geometric random walk with normal increments adequately describes dynam- ics of both biomass yearly averages and biomass on individual forest plots. Using Bayesian approach, we have generated comparable USA forest growth rate diagram. The other direction of my research was to model the change of spatial distribution of species under climatic changes. We investigated how various combinations of bio- climatic characteristics affect the potential distribution of Pitch Pine tree. We in- troduced two novel data-intensive models (VIMM and VNM) and calculated Shapley scores which reveal the most important climatic factors for Pitch Pine spatial distri- bution. In continuation of climate modeling, we investigated how forests in the USA are affected by various climatic characteristics. We performed various dimensionality reduction techniques (stepwise regressions, principal component analysis and random forest algorithm) in order to reveal the most influential climatic variables for forest biomass in the USA. We have obtained that precipitation related factors are the most essential for forests in the majority of USA ecoregions.
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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) | 0.001 | 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 it