Multiscale modeling of longleaf pine (<i>Pinus palustris</i>)
Bibliographic record
Abstract
There are few remaining longleaf pine ( Pinus palustris Mill.) ecosystems left in the southeastern coastal plain of the United States. Restoration and maintenance of these remaining habitats requires an understanding of ecosystem processes at multiple scales. The focus of this study was to develop and evaluate a modeling framework for analyzing longleaf pine dynamics at the spatially explicit landscape scale and at the spatially implicit population scale. The landscape disturbance and succession (LANDIS) model was used to simulate landscape fire dynamics in a managed forest in north-central Florida. We constructed a density-dependent longleaf pine population matrix model using data from a variety of studies across the southeastern United States to extend an existing model. Sensitivity analyses showed that the most sensitive parameters were those from the original pine model, which was based on extensive observations of individual trees. A hybrid approach integrated the two models: the fire frequencies output from the LANDIS model were input to the matrix model for specific longleaf pine populations. These simulations indicated that small isolated longleaf pine populations are more vulnerable to fire suppression and that landscape connectivity is a critical concern. A frequent prescribed fire regime is nonetheless necessary to maintain even large longleaf pine sandhill communities that have better landscape connectivity.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".