Data-intensive modeling of forested ecosystems
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.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".